{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c641bc9a-087d-402a-861f-fb97011b1eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "from patsy import builtins as pb\n",
    "from IPython.display import display, Markdown\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539300b1-5d7a-4e60-bb99-a687fd05bf2b",
   "metadata": {},
   "source": [
    "## 1. Coeff Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9114afa1-7451-4612-bbf6-992aa2274260",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_8428\\2090342771.py:153: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x620 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_8428\\2090342771.py:178: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9zDPPPI3vfe97HS9zK0cfffTA+3n+gx/8oDH//PO3/c7111+/8cADDzRGw7nnnttYfvnlh/yNc801V7UfDfbZz362o30lv6l5HQz1uOGGG2b6jptuuqn6vcN97m1ve1vjkUceGfK3nn322cP+1jzWWmutlttmuEezyy+/vLHMMst09LnLLrus5fI2r7Msx+xo/h3t9vlO5div57Xddtu1nW7zzTcfmG6fffYZcpny/Ctf+Uq1r7VbTxtssEHj3nvvHXLZTj311MZiiy025PrO+5mundnddsMd7+2OoZxzBstr9fuZ9oc//GHLc0PeG63jeqw0r5dWv7XZJz/5yYFpP/CBDwy8nvND8358xhlntNzeg/fxI488srHAAgsMuT5WWGGFltuz0/PcSiutNMtnn3zyycbee+/dmDZt2pCfnT59erXfDeWee+5pbLHFFh0ty1VXXTXwuU7PX4PPMfXr2W7t3HnnnY2NN954yPnmOrrTTjs1nnjiiZbzGLxNcx1fccUV285v6aWXbvzpT38acl0BjHWdaSzKMHHllVcOfCblrdHWSXlw8G87+OCDW/625uvG97///Y6uNQsvvHDjlFNOGXIZm68///jHPxprrLFG2/ktuOCCQ5YpTjvttKr+2cmy3XXXXbN8PuuoXR2ltsoqq3Q0/ze/+c2Nhx56aMj1PdxjqO00lmXjweXVgw46aMj9/S1vecuQ7QTN5dtW5aeRyjyG206d+Nvf/tZ4xjOeUc0n5cb/9//+X8vpLrroooGy3ZJLLtm47bbb2i5T/t58882NNddcs+36SvvLMccc0xhPKVeuvfbaA8vwzne+s21ZLXL+WmeddQam33LLLWe7XWfwMTZcfffwww8fmPY1r3lN2/NGzqOtypKDy7Szc2x0e9wCdFPurB133HHDnrfq6/B4tNE3t61nuf797383RstkbWu49NJLq+mGale6+uqrR3wfoF25ZqjH4HbA5rJHO2k3f+tb3zrsvDPNUG3so1leH8/7L8Pptv2vuTxz7bXXVvcoWn2uuZz6vve9r6Pveu5zn1uVUYdaf837xG9+85shzxPPec5zGrfcckvb+X3uc59rzDnnnMMu1wte8IKWnx9uP7z++uuHbSOtj+8sSyvN63uox+CyXyd1u9h///2HXcb8/uuuu66jffE///lP4+1vf/uQ8/viF7/Y6HTfnN37Os3nxtmti+W6VM/r+c9/ftvrQH5f83ZJe3m7Zcrvy369+OKLt11fyy23XFXmH0+5hjzzmc8cWIavf/3rQ06f5Ws+Fr/0pS+NynKMpO77rne9a2DaL3zhC7N9v2l2jo1uj9t+2ZZpPznssMMG/p9rRDv1NLn3107KRikjzTHM9TVlrZS5hlvG/M3+9sIXvnCWeTSfR5rPfWn/eu1rX9v2u5/1rGdV9/4j185nP/vZbafddNNNq3NpNwzcN4zf/OY3A8/TA2so6TmXqPH0vkpUWCIJEw2byLRE2Kdn7fHHH189T2aLRH5mXL7h5HsT5Zgo50RXj2Yk4TbbbFNlXUj2iERmpxdBu+HLkoUjUd2JtB9O1kOOx2RZSbR5IuMSuZmxItOTNdHBiSZP9FuyMmWdJdPH7Pr1r39dpeRONF6iIBNxl6jkpEhLT8GkMUvKuY997GPl8MMPn63vOv3008ub3/zmKkNTeuUlEjg9KBMJnd+f7Z0IwXxn3askkb+19AxIFGBk7Oy77rqripQcvFzZJul5kAjz9OhIdqvIek1v3Wbp8VdLpGV6Gtx+++3V/xNlnN4ymVeWNz12snz/+Mc/qsw4yYaTHsSteu5kW2211VbliSeeqP6fHtT5f7Zv1m++I5m16rFBa4koTYR49plkKYlkhsrr7SSbSJYz+0mkN2t6AmW9JnIyx0+iVZORJ1H9U81XvvKVKsvJX/7ylyqyOMfUu971rlmmybqM9PhuFbE8OMNU1nN6fSa7Tz6TCNZEyeb4zT540UUXVb2s8t2txmzNuS77c72Ns+9kH805LNs3mdXSwysRwpkuqZWTrnCqbLv8/owRn/07vTTSUzbLdN1115UVV1xx1I7ryaC551eio1vJ7377299eXVfSYyO/MZHvN91000z7RzI/7LDDDgP/z/6abZxI5vRw+OEPf1jtyznf5NqU9fyiF71olvNc9p96uIb999+/6gHRrFWK7mynOvtdetKk51z2qVw78xtzjGR7pbdVzofZNjk3DZZ9Nj256qxR+a5Eiue1/Oasgz//+c/V9SP7Z/M5LMdVZD+O9CRIFojBWl07h5Lh5ZJR4q9//Wv1/5z7E8GfckOui7lG5rdnP0w0f6LIs66Hkm2QTHC5lmSbZltlvWU75bqSbf6vf/2rWo+5JqaHEsBE1JlGuwxTy/Un5/iUR3JOn2ip9+U6lZ6ryQSaLH459yZlc3oP1bK8KZ/kGpBrQ3qn1mO25xqX3l35PbkW5Pye9THcdSfTpodmrmvpzZ4ebalvpUyXXmvp9Zqyf67TmWZwhtn0vM21Mter2GSTTar5ZblT9k+9JNfO9LKenayM+e3poZe6Qeoq6c2VbZjlT/03ZYcsc7IUJUNV1mmzbuoanRqNsvFgycxz7LHHVtf99CLMPpvyZrZpsrymR1rqXWkjaM5KMBUkY1TKLNnXs12zrpJtp3nY7WQuSPtD1mndgzI979pJOSh1ztQbUp5M5p6U21OeSZ0/5aWUz7NvZD/KdhgPOT5zXKbNJ2Wq7AtpC0pGosHnqOwfKWen12Vdpsx+Pd7lsE7qB+nFmHWYMmXKkfV5I3W75s/P7rExlsct0B9GWu78xje+UZ1nailz5HycjIq5Dqe8lWtx5tvcHjCWbfTNZdWUuXLvYDRM1raGZNtImbTOTpf25JSFkkEh5cr05M91Ie1N7a5TncjypBwyVDt7pLw9EskKkXWQrNSR8n2WP/cdsg+lTTxZYbL+81tyDUxZoc5YNRbl9Ym8/9LK7LT/NQ9Dk/pT6gQpU2Y75Tdm/aYOUss2Tv0xWQ9TDsm9jrQVpiyd/THbIMdpjoPUI7Mu8/5Qsu8mU0OOjWzbzDvZnHL/JMdK1mfqPTmeWg1h+Ktf/arKiBQp/yYLUuaRfTDlxGzXlAeHyq46nGzHlL9SHk45NJm86iyQ2d9T/krG/9zny7Kk7TYZO5vlXlKOs2S9qkc7yH3K5ntKkf1opJJdrG4zTVk/54vs2/V6zDGSrGBpR069P9sl5cehpI0g5f7sR6lH5Lyd4yLtCNm34xOf+ETVxpv1PZXkmMn+kHaPtBEke9Tg81X2/QydHNmeqW/knNNO2vWz3nMOyrGeR+pJyXyT80qOj9T1Uz/J+s+xNh7SfpFzYr4394dyTc5xMfjeZmS56uniS1/6UnUvd7x1Un/q9H7T7B4bY3nc9sq2THbFZJlMeSP37uqMUN3INaC+JzX33HNXo/ikbJVzba4tOWZzzr3ggguq1y+77LKqXNBOrk3Jqpc2tJQjc08umadynGabt5I2jlxrco1L+Sr3JnPs5hyRMkHu6Scbcs6DWccpy2a+2a+yv2a+qWtn3SdrVjJIDs5e1pGuwo76RCLNEuldR8F3Mn16ag6OAm129913D2SESc/DG2+8cdj5NkfWH3jggY3RzEAU2267bfX/RA7+97//nWn622+/fSAyss6C0kkGovyu5iwOrfzhD39oLLXUUtV8EiH31FNPDbvMrQzuwZZIzVbR4Ndcc81Ar8i55567cccddzS6lZ6SdeRkfsPFF1/ccrosRx1ZmO2dZWilk54OrSKK28m6bN7P0uOwlWzv9Jis53nEEUe03JbNvY/322+/tj1UEsnYqmd0p71844QTThiYdo899hhy2r/85S9tozwnawaiSK+cOjI5vcnTg6A5ijc9Q+pj8p///Oewy1Tvh616+6TXbnMEaqtzSCJam3sB7bvvvrMcj/n/Zz7zmYFpMv2DDz446tturDIQ1esovZnG67ieiAxE6QGX61U9bbIatDp/5JHr229/+9u288qxX2ceSsR1MhENlgjv97///QPzzHppdQ0cblsN1pwd741vfGPjvvvuazndz372s4Fr1IYbbthymny+ntd6663Xsqd77Xe/+1113RtspJH8w02fKPZ6mhe/+MUtMzckq1JzRP9Pf/rTWaYZvE1zrj7vvPNaHuPJDldPl/UGMFF1ptEswwx1vWx1Ph/PDER1L9V2Zbnan//854F6WTupf9XX5Fe96lVtp2v+7lwfUzZrde1+/etfPzDdIYccMss06RVWv/+1r31tyGX7/e9/37I3Uye9VFN3Hqr3fnpvJxNAPZ8LLrig5XQjqWt0Mv1olY1b9cbOPnr//ffPMl0yYNaZiZZYYokqu/FUykBUe/e73z0wv5R3mjX3Hh6qrjA4e8Auu+zSsv6ZDF/N55HRyjI8kkxEzeWrrbbaaqbsUYMzFY1W5qFuMhC97GUvm6k+36x5XWcf/NGPftR2PqN5bIz0uAXoptx5ySWXDLQZ5G+79tHIdSRlrvFoo28+Lyd7wWiZrG0Nzb3ot95665blnEMPPXSm72zXLtjJ9aPTdvZOp2/OALLyyitXmYMGy2vNZZiUU8ayvD7e9186NdL2v8GZNVL2Hy5TQvbFoTLU5jfm+KznmcwfrQxuN05mltTNBku9MtnPhxrFox5JINu03Sgf9Tnswgsv7Go/TNmyXX2o+dhNHTTzWWSRRVpmcR1JRqFOp0+dsM6+lAyhp59++izTPPzwwzPt15tttlnL7xo8EkfqDa3amrNd62nS9jvVMhDV6ySZsup5Np8Pck1KlpH6vV/+8pfDLlNdnj/22GNnmS7HVeoj9XSve93rGuNt8PUn2aiHym4zWtlqRlr3zT385tEYmq9/I73fNJrHxkiP237ZlnUdPPXY+rU3velNLT83XAai448/fmCaxRZbrOX5PGWr5nr+jjvuOOwy5pFs1EMZfO4bnMWq3leaMxmlrJf9Khn0Wt1/rUfMyrZo18Y0FAFEQ0hDbrs0z6M1389//vPDTp+TTD39NttsM+oBRM0nvaTOa5adur55XAc5dBJA1Kmkz6zn1a7wNJIAohTSkrKrnU984hMD0w7VMDac3XfffWA+559//pDTJrigbgz+8Ic/PC4BRClQDFdIruXEkcpPpk2BZbAsc7tG4E6NpHGwuYCfk1y3xiqAaCSPoSpKzQESL33pS6vG5BTqm2+UnXjiiR0v069+9ashUwXWBZU04A6+WOTGUD2fN7zhDUOui+bCzFe/+tVR33ZjGUD085//fFyP6/EOIEoltPl4TaNac2PR4ArF4ILdYKmgdXLsp5DWnHL5F7/4xWw1ICSwcdlll62mfd7znjds4aZ5yLZcL9tdP1PZ72QInFZGM4AogXN14S2VkaECiX/yk58MzCsF08EGb9Ojjjqq7bxSQamn22GHHTr6HQBjUWcazTLMYM3XwVYV6PEMIEr9aTRTg6czRz3vdqnzm9drbuAPVY6pp3v1q1895JC7ww1z3M5oNW6l4bS+Udju+jXaAUSjVTYeXAZKA1QaI9t5xzveMWzdeCwDiDp9DFUeGlyfqcuFzXWfDOc11LHcvEwveclLhuyg1dwInrTl421wEFGCpFI2znZufn20g4dGEkCUlPv1dDmfDq6nNW/b3XbbbcjvHM1jQwARMB7lzuZz0XDD3HRitNrom4MR2nVeG6nJ2taQG6v1+xkGbajh2gYPVTQZAohy/V5mmWUGyveD252aZVvWQ560q7uMVnl9vO+/jEcAUW7otgt46cZGG21UzXe11VZr+f7gduOzzz677by+/e1vD3k/b/XVV6/eW3fddbte3pHut+2ks+hw979GO4CoeXi/oc616UhRH095tAqAa94X87xd0FvawbPPZLqc+9oNAz1WAUQjebQKBKhlHcw777zVdIsuuujAubu50386VHS6TEN11EjwQXPH46ECX8Yy8CS/s16GBI/Wr49lwEmnAUS5RiX4pJ4u1+vmetxI7zeN5rExmQKIJtO2rI/9nCuahz9PR/HBmq83rTQPPX788ce3/f6s/zpBRI7ff/3rX0Mu45vf/OZhf1PzuW+oMm5zoNRwgUnbb799x/c7W2mf74wqFWetTiM/GpJWsU5bn9SCw2n+7uZlGi1JSZ/0f5F0ac3q/2eaZz3rWaP+3c2pBTtZF8NJWr5WQ9jUkma1lpRh3ch5JmnQIukRk5puKEm3mSELBqfZHUtJSRhJ79kqfVyzpF5LCshIas+kOmxO0Zo0+/W8hhtKazQkzV8tw1d0K6nZ/i9Isko7N9lkKLqkK2xOR5m0okkpOfj94WQfS6rbdpKeud73k1Y4Q4A0S2rXWtJ+DqV5OIXmz43mthsLSck51LAGU+G4rmX7JT1h/Uhq4KQhTKrgDNVY22+//dqmrkx67OahyVqpt29Sa/7v//5v2+mSGvnjH//4LJ/rVtIz1kMvJn1xzlFDSYrQ2uBt0ZyKO7+hVcrn8ZahIpOCOZKCcqh0sUllXl+fk+o4aTjbybZO6sp2ks66Hlaj2+sfwGjXmWa3DDPe9aaR1nOy/KM5v07rTUlrnhToQ633pExud02YTGW6DDeQ9PyjVV/sxGiVjQfLUAxJPT87ddUMlVLXcSZ6H28lqbszdEVdfkvq7QzTkmHTW70/nKQ4HypNf3MZNcMbjLd6OLMMDVMvQ1KH18ObjdewZUkP31w/yPAVhxxySNlggw0Ghj2I1DczvEk7w7UdjNWxATAW5c4ML1G3EaQdPu0Ls2u0ypoZNrKW4WV6ua2h+Rqw4447DjlcW3Pb0mSRIWoyfFVdDstwIu1kaJKsj7rukmHuxqq8PpH3X8ZKyo1DDQPT7e/OcELNx1wraVOtt1035fS6/nT99dcPDNU3UcZ7e2d4nlNPPbV6nu03eNi0Zhlap/n94cqIqUOkXbqVDBFYb7Oc+7LuW8n9rLr+dMwxx5TJJvWIDJ/XPORzhqdKnWnw+8PJeSVDN7WT/bR5/U9E/enFL35xNXRbfe3bY489qnPheA9blvsPzfWn4447rhr6L3WlDEkY2fe+8pWvtK3HDXe/aSyPjclgsmzLWrbXgQce2HF9dbAMI5d6dSQW4u1vf3vbaVdeeeWBe+rN27nbevZgzUPvDnWOz3kww7e103yPM0MljtTMA7Qzk4xBVxuqoW+wjEWXm5ZnnXVWtVFysGRs1lYyNt1wmr+7eZlG88BKgEXGYkzjV8Y2XmGFFapCbsZgjIzv2o00mmW8wIsvvrgKjMj4vjmgul0Xw8mN/6HUBe+oT2IjlW1aFzpzM7oeb3Uo9ZjHqZClQJOxcMdSxjiOjIObMaSH07wu8vvqMTozpmO2WaTxMeP2jrVcYLJPplCXimUK+TkZj3R86rGSgmmnJ/zB4zwP9r3vfa9cfvnl1cUpF9NaxudM4WQk66yTaeqGkwQsvfKVr6yeZz3n//U41MONF5xxWFPYzHjDGd8zY5zWjfqTedtludtVOKbKcV3LuXooKdSmcjRUhSE3NIeqlGec37oxLEGZw42J/LrXvW7UKqf1+SseeuihYbdFxidvVxDKWLS1oQr04+mSSy4ZeJ5xaYeSfTbTfPvb3x5Yt6usskrLadddd92BRrtWEgSahr80fHV7/QMYzTrT7JZhJqLeNBLDBSMPluCoNFrlN2Y881wDm69xI6k3rb766sPeVEu9KPNpdU3ItefQQw+tnmes9jS8bLXVVsOWB7qR7//xj39cBZmkMT7lsZQzZ3QOG/364nBGs2w8EXXV2ZGG6qWWWmrY6doFqNfWWWedctBBB1UNiDkON9tss4H3vvWtb5VnP/vZo3aOyE28hRZaqDpeEuw21PofKznvpO0nQUNXX311+eUvfznw3ngED8XXv/716jFUmXKXXXYZso653HLLDdlpbCyPDYCxKHembFWXJ3ItGo1z8WiVNVuVc3q1raG+dkSulUPJd9XX9cliJOu1bh+r2+KzXtsFpcxueX0i779MhvrTE088Ud1gT5thfn/ux2W/SXmj3e8e6pwxu+X07Bu58Zzzwstf/vIqyD0d3UcrQLBZ2tvTCTbtp7l398ADD5T//Oc/E7a9U/6t97e6/DfcMZJAjU7akCdz/Sn3yQ4//PCOph3u3kjK6QnESOBKjuM86jJ3Aolynu1Egl9Sph+ufrX33nvPcn4eTy95yUuq35vAvARNfe1rXxt4b7wCTtJ5fyi5b5LlGiqIZLj7TWN5bEwWk2FbNkt5L8lQzjvvvKoceMopp1Tn4pFe7/N75hhi29bb66ijjhrYXu1iKHIPMffWRyIBye3UyWnqskSCzzqZtptzpACiITQXtFJ47bTRLQ1l7QKGBquDM4brdVlrVxiYXQkg+uxnP1sVshKJmxvU9c6f7x/qRNmuELfzzjuXI444ouNKUSfrYnYbM5svtnWvjJFq7mGS3h15jEQKksNdyGdHGujuvvvu6nmyCaXRciSaK8DNhcyheiqOpuc973lln332Kfvvv3/1Wz73uc9Vj2WXXbY60aYy8frXv746OU6EBFe95S1vGZV5pRKRDE+5qOWYifTEScFwqB45g3XSCN88TSpVzcddfb5K76PhGnXzfrKopQCU81EKBnWFdzJvu+YKxVQ8roeSHtzZl7Jesy+lsDJcxrjh1kedASiGyupWy82mFFZSaW3+bDeat8VIC5mDG/Dqc1gK6HVg5EQb6bptnmaodTvc9a/5Gtjt9Q9gtOpMo1GGmah6U6eGu9bWHn744bLtttvOFHQwu/WmkVwTWt1cSENIsuWkYTr1ivQGzyM3ltKIm0bxN7zhDVWnk9mR37z99tsP2yN4NOuLnXzHaJWNJ6KuOjty4yM96UZDsjykc1JzmTrZC4bKYDBYgvqHuzmcRr1sp9w4ynYbav2PpSxnAoXSESRZfOvycW7mjXXwUCu50ZuyedZNAn3S3lNn8ur2nDWWxwbAWJQ7x6JNc7TKmrlu3HrrrdXz0cpWMlnbGprL77kudHpdnywmw3ptFww0UfdfJrr+lKCZdHIYSTaF2a0/DVdO32uvvaoMFH/605+qR8q8KQu96EUvqupPaa/Nje3mc0g30mH0C1/4wsB9hMmwvcfqGJns9acE94zWPaJ6JJhkG2q+dqWDwEg6Zo92+8pYB54kwOQDH/jAwGubb775uAecNO9LqX+mPpcgq9SfmoMvxuP+ykiOjclksm3LdGaqgw+TFTdtV510ZBmr7bX44ouPOPHAUO0gzee94dpLZvccKYCow5XbycU2aaKb00XVhYM0tKbxpnl+H/zgB6tUqnXj0lByQ7Y2kqCCkcjJLlF16d2bAKKkBzz++OMH0p7mgjgSSVVWR+CmwSxBAxnuJ9+TG7l1I1qyXNTRnp2si+GMR4+22a3YPfbYY2WqLF/zfj+aKUSHk6CT9DrJyT6pYuuT8M9+9rPqUUftJi37UKljp4L6mKiP82QtGmnDxnDRy4Onae7J0/y8k/kM3hfy+eaG4Mm67YY7d07247rZOeecU6VNHsv10e1+kf04N0Jnx+xsi8HboT6Hjef5azgjXbeDj7d29OgGpkqdabTKMBNVb+pUp9+fulYdZJHfmoaWZApMYHLqYM1DQtRZCIerN43GNSF1wmR7SiaiZCWtsy7mkQD43OBJI3je7yY4PL0q00mlbvxO43oa6nJjKQ132a/qHl8JUP/LX/7StkfxaBrtsnG/Xquz7dKg1hxAlOFSRqLT9T/4HDERQSoJtMvvaz4209bxP//zP1W7ylgHEeWGw+wO2z1W9YPmzwsgAsaz3DkWbZqjVdZcfvnlBwKIkk1kqJ7mU72tobmNqJP7C51eY8bLZF2vE3n/Zax0ckzlGEwdpQ58SJ0p9ad0as1IDLlJW6/bdNBNgPd41J9y7y/1m9xET0BXli91lwTD5ZFsX1m2dJ5IANBQGSPaybz322+/geVNdqu0safDZAIqm4cIrjuUj8f2HqtjpN/qTwkuSyfsOoAo58tOM6eMVfvKWEqG0sHDViYIL/v5eAxnmXaN2e08M9r1p5EcG5PJRG/LwVKmyjnw5z//eRXQmU496SA3nLHaXvN3UV7s9Nw31udIAUSjmJa0HuM9Kamyc77xjW9sO21zNN5wmntkjmWDS8aZTQBRUuYner0u4Of1kcgQaN/5zncGKkS52d0u+jUNwVNN84kh2aYSCDFZly/p45PKvVvNUfGzGxQwUjl+8siY0RmGKIXwpJ5LOtD0qsgQe8lok0bpToa/mIxSiH/Xu941UwNELrgHH3zwQCrJTiTbz0imae6l1fy8k/kM3hda9fgar203mpWgyX5cj7fZ2S9mt2Gu+fO5YTlcb+nhzmG5fo/3+Ws01+1wxxvARJudoRxmpwwzkfWm0ZIyUR1gkevdGWec0baX23hnMUkAyHvf+97qkeF2k/45Zbpzzz23qsOlTJdlT1kvv2Ok1+uk5q6Dh775zW+WnXbaqe20aWQfL2NRNu5Hp59+ejnssMNmei03rlK263Sow07X/0jOEWMVPJQhWepAuwQSpU0lQ1VnWI0M/5cgouabOlORYwOYauXOsWjTHK2yZtrE6qFjUo5KNspebWtobuNJJrvhhuLp9BozXibreu31+y/tfOMb3xgIHkqgdkbQaFfGyrE1nnLDORmCMspHblrn+9O5NkPdpnNtsj+k3pM28gx3M5JguXw2nXbrYyrzTLDYZDiGJusxMtWk00zuCzWfL1MXT517uKGUxqp9Zazk+pfst/U9sQz5d+SRR1bX2DxP8F2GUZ/q+uHYmKzb8oADDqiGBMz9w7Q9pePecOWPftheI9U/IZxdaB77d7hKSSIW02MgkrpuqOCh9IAYSeN687SjlVK8lTe/+c0DlZ+kq49Eb4+0F0RSlde9Q5O+cajUeVlvU01zaroU1iebRLDXlbPZHee2+beOJC3oaErvgfROTkBHGmEzvFEaYePxxx+vsmVNValU1JWZZJSpG0JyUWsec3M49bmn02mah9pKg0pdYUlD93A9u/P+9ddfPxA9O9RYzt1su+YL+XBZfeqh+vrhuB5v6fFQy/jpw0lvprqgOLtDuY3mtqjnlUJfhnSciuv2b3/728DziRomD2C06kyjWYaZyHrTaEnAUHMDx1Apsiey3rTSSitVDfRpsE8mpNQLkmm37mlVd6TpVMqBCUSKF7/4xUMGDw0e3nSsjWXZuF/ccccdVWN3gszSuapO7Z+bPRlqt1P33XffsOeUfEe2U91Ld7zXf7JKpxd8HTyUziHJ0HXmmWdWWVnrofpSJxrPjKVjwbEBTLVy51i0aY5WWTPDxdYSZDoaQ+9M1raGBLd0WvZvvq5PFpN1vfb6/Zfh6k/J0prhnYYK0J6o351gj2RX3XHHHcsPf/jDKttYlrse/jn1qTr4q1PpyFHfrE5QfrvgoYn43ZP1GJlKcjx/8YtfHDhn1vdjf/3rX1cZfyeqfWUs5J5XHXCSYyVBdelMn6C4esi6nNMOPPDAMtX1+rExmbdlhv6r2x7SIe5b3/pW6fft1Q0BRENIRaCOHLvmmmuGbSTrdDzfZPkZSfr15kpOxsEcK7lxn4bhZiNp4OtmXaRn4lSz1lprDaSZTHR/u3GIJ1LdoJ+b+rOTgSiF3fq3JmI+DaTdaE6l1umYzO0kLeePf/zjsuSSSw4UukdrvPDxlJsmuVEUuVmUBoP0moj0xt5mm206HgYkDdSdFERrzUOH5eJeN24nyGK43hl5v66w5HMjSZPXybbLkBW1Op3zUJWnfjqux9NSSy010BiWscVT0BruulZrNTTdSM4B9flrNK4RL3/5ywee5+ZNt+qeHrN7/hq8fppvHHdyfE/1IRuB3jSSOtNolmEmst40WqZqvSmdTDI0bX19TxaikUgQeJ19aLjfnV6YwwWNj2ZdYzzLxr0o6//d7373QL0xPbBTz6lvcpx88snVzZ7ROkekx2NdZ3rJS14yruu/Dh5KD/NIW0o6YiVoKsEyWfbm3/22t71tSgcRjfaxMZrHLdA/RlLuTJafui6fclQCmGfXaJU1k427zt6YoKSvfOUrPdvW0BzocPbZZw9b7hvpkMit1NeYiWjDGa59rNfrEWN9fa9/dzrhNrchD5agvLrDwkTLeeg1r3lN+drXvjbw2kjrT6O9vUdzO6VNve4QnKy1yZwzGY6RqSL371J/ynbIdsm9kwy9Vwffp7NORnXoRLKN1Rm6RqN9ZbQl89bggJO6M1Guqc2BJ/nd9b2zqWq0j43JVH+aCtsyyRvq4cPy/cOVL5rXeSdtlb/pg3NZf7duDSMHZN1AkkwuQ518m1MODhXpmQajz3/+8yM+GGujMSbyUDK0Wnb2PPJdnYwN2O26SI+C73//+2WqSYNgHWiVxuyRRAGPl/T4bE5/2O0Fpfm3JqAiJ93ZTVc7Gmk0M4RDcw+W+gbEVJEUfklPnEDCXGDTGyFBNW9605vKLrvsMtBb4MMf/nBH8/vrX/9ajSvaztVXXz1w0Uuw0kYbbTTT+2nMriVKeCgHHXRQy8+N1rZ7wQte0LJAO1gKIfld/XRcj7d6++b8kTFr28k2/PKXvzzL57o9B2y22WYDQWYJquuk90Q7qYDV0pMjPdm7US//aJy/MjZ7xj2vx2MfKjjrhBNOGPj9a6+99ky9LQGmYp1ptMswg69HdeB8bi4lE+Jk12m9KUHTkymAqG60r4cGGWlZvNPfXQegjHddYzzLxr0m66suwyfD6qc+9amq/H/ccccN7C8f//jHBzL2DCdl8qHqss1l0GT5Gc+G/le84hVVh4hI3a4OHqqlc0JuNNY3Tk855ZTy1re+dUp3VBjNY2O0j1ugP4yk3JkbV2lfqG++f/WrX53t7x+tNvq0BTYP0ZryTic3q2q5Yff+979/SrQ1bLnllgPPv/3tbw+ZbSkZzEfDaLbhbLDBBgNZCdLhsB56rpW8l2nqusuGG25Yxspkvf8y1tf3+nenLDbUzeAMpds85OBk0HycjVX9Ke2enZzrRnM7JQvUFltsUT1P8PhQmT6yzXIeqPV7/Sn1nNzHqwPEUndKx9p0wj7iiCMG7ie/853v7GgoztxrGiogNQEszdtnPOtPuX4m+172gVwDsx8kS9fgZAYJNK0DT7I+RnovfTIZ7WNjstSfpsq2zD3Ij3zkIwP3+4a6v1Vn3k6G7Egm3RNPPLHttCljpawVCRJLGawXCSAaxutf//qB50MNKZSUWPUBnLH1WmXG+M9//lM1LHXaWDa4cpIeFim0jqX0fsj35ZHf0E3De12Rqxv0WhXWMpRMhnmbqg1FiZqso4AToJOC2VBZpfI7v/e971UNp+MhF/866jGpDhMINlQhI2NBZrpWJ/GMUVk39uYil/F2M30raQxtdXOjuYA8XMR0ovFTkR2qd2Z6M9bHUVIS1xeiZmmwzgUsj2OOOaZMJsnsVWfXyVig6XnUfMzkAhvZX44++uiO5rn99tu3TMmcxpSM8Vlvs49+9KNVI36z7bbbbuBYz028/fffv+V35PWMuxuZfnCGstHYdnXjTqQxpVXWq0TTdxPcONWP6/GWAlaGgogUBFsdR6nwJrq83qYvfOELBwrG3Z4D6jHD64pNCqR/+MMfhvxMKs977LFH1YDQLDdsMjxn3bj4hje8oRp3vJ1c+5p79Qxe/tzozrV8dmSfz/Fa/76cr1tdJ/Obm4MI995779n6XoDJUGca7TJMs1yL6h5dzcNCTGbN9ab99tuv5Q2V/K5cK8azh1mWZbisuSn/1Jkk07NuJFK3eM5znlM9T9BXq4aZbPcMd9tJ4NRIyhmdGK2y8VhIT+q6jjPZhunLsf/pT396IMDsRz/60UAvyWc961kDQzWkzphG8OF6YNY34rIftNoXE1xU7zvJntnciaZZvb7yGI3h8FLeTOah1Eki9ZLcmGuV/agOIqrr5tmfciN1qgYRjeax0c1xO9rbEuj9cmfaFjLUUd3mU9+QbSXDsqaz2ni10addPEG1kexIufmU4Myh2hwyXdqh0n5/0kknTYm2hrRxJktBfcMtgU+tskElQ2GyFo6G+hqT3z+7w9mn/pH2pkh5PGWYVtegvJb36jJ7PjPU8Fqza7Lefxntcnm73531nBvS7eopdZl0vKRT/nD3/JqDA0Zaf0qmzTqjWs4B9TCxzZLNLEMHd9KhZ7S3U85ldVk46745M0ct56UM91u3y6aNtr4HMpZyHajLjynLTiapz+SeXCTgsLnzTM7hH/zgBweGU9p55507mmcCiFqdS1P/SH2pPifmWlpnwhvr+mbuMzcHnHz3u9+thuJrJcuUwJO6c3H2p9yPnKpG89gY6XFrW84YQq2+39dJtsdMX/vQhz7U8p5Urrc5Puv2jJS90h7Ri2aUoGkrjTu5wR/Z2Zuj5pulQJibqMlwkEJwIkVzQcrNy9wMTaN4GpVuvvnm8qpXvaoaEiY3M4eTnbFuUE9FYqhG88li/fXXrxrIUolLxSDBVbnYJdV9GoJT2UrGlRRes44mW3BHp9GLuRCnEJ6Lbxo1E1yT/eP5z39+FUyWimeyyFx++eXVvpMbAu0a2EZbTt4ZViDbIvtcGm/TwLfVVltVUZSLLbZYtTwpUNY9uxOokX0zgRPNEvGcfTefTbBACjJJpZj/Z9tm3//Xv/5V/c70qsx4vs1BIPXFIg2HmS7LkkpteurUwQnNDQC5+OX70tiai9E666xTre/6e84777wqSK9uRE4DwHhKIesXv/hFR9PmeB0cfZoKcVLYR46TwUFbiVhN9GoqBrkIJYgjBcj6Jksr6cn685//vNq2OabSiJHer1mXRx555MDNnXzfnnvuOcvns79mnWdZc4x+5jOfqW7W5KZdetkkoCLLVAdGpvEl09dpo2ujse3yu9Mgn2Mm54/0hErDxuqrr14dU0nxeuyxx1brNoEhszMs1Vgf19kWdS+fHDfdZvCaKIm6TlDYDjvsUG2zNPxnP8h6z02hbJ/0tK57Xudal3NDq5snGUos+0GCy+po76SzrNN4Jp1k89BluZ7mhmIyEKW3VPbt7FM5RyX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      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_8428\\2090342771.py:204: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5pIdvvjvBPek5nGHC8juyDH3dfPPN1d9k2RnMTYcEx3z5y1+uvjcZmnKzKIFSucmy0korleGUmypZz4cffng1LMVPf/rT6vs33XTT8j//8z+D6u0+00wzVb8l6yo3cnKjo86YlHWUG0f53c1kHSd4LIFGuZGRGyUJwkrGozzmmmuupp/LvnfbbbdVN9YSyJTfsuCCC1ZZi77whS9U+yIAwPSsm+XtWrPhgZ944ompXq8Dj4YqZfxk6knweAL4U85LWXS77bargqeaDX0c6WiQYJZ0IMjwxwOV9XLhhReWgw46qJxzzjlVptOUU1OfSHk7GUOHU8rDWf5DDz20CsBPRqJkmkrATrIMJdhnMOsyw7gdffTR5fTTTy8XXXRR9TsTTJWOGqkv9ZXgoE984hO9dawMr5YOBQk0SgeAXXbZpeUQewneT7k6AU2XXXZZlYkpZfTsd+kUUg83DePRDD05Uhj3cvHLyTlRnRkrkgFKmsD6gpqUd5Mnj/YSATAdcR0GxgrnoyFQJ4CJI71VEwCS4cgSvJGAkiWXHO2lmm7kBkUyG6U38Lnnntvx59KDOL1/l1pqqamGERsvXIMBust5dRio8zBSlLeHJIEw6RiQYJYE1HQqATgZOjlZMfMchsJ1eOJqP+A6kK5mpSTaNI88BwCABhnzPpkHNNCMY+oEMP7dcEMp73xnKUsvnXHEkjpmyt/8/13vytgIo72E04VLL720ytiTYQoAxjrleGigzsNwU97uWnk7AR3J9gMw0gQYQScWXXRK9H4eeQ4ADIv0Vk/jbn+PpdPowJhohG98ZOiQjC2e95LSebgkXXO+b6QzG+SGQ743v48JSp0Axrdk2llvvVIuvriUOtn3Sy9N+Zv/X3RRKeuuO2U6+pWhBDJsQ4ZRY+JQjp8+KMePH6ecckrvNsyQjs0cccQR1fuZFjqizsNwUt7umgxtlqwx888//2gvCjABTRrtBQAAgL4yXnqrMcTnnXfeEV8emttqq62qMcfjgQceKD/72c+qRo7zzz+/GoN8MGPdT68ynvt2221XjbcOwHTYk/p97yvlxRdfvtnRV96bYYYp011zTSlrrTXSSwnTBeX46YNy/Pgqxx955JHlwx/+cJlvvvlGe1EAmlPeBhg3BBgBADDmLL/88uOuh+lYc+ONN5bHHnusbLrppi2nOfXUU8tGG23UsrF96623rhrja1/96lfLG97whnLLLbeUM844o+qlPFEssMAC1QOA6dChh0650dHqZketnibT//SnI7V0E0ay2/S02waMecrxw085fvyU4++8885y3XXXlR122KHlNBdccEGZPHlyWX311VsG9d1xxx3lS1/6UjnqqKOGcWkBhkB5e0xI2UB5GxgqQ6RBJ/7971L23XfKI88BgFFXp3v/yEc+0vK9PfbYY5rhAJIi/6STTiqrrLJKmW222criiy9e9tlnn/Lkk082/Z704n3zm99c5plnnvKKV7yirLbaauXoo4+uhv7o64orriibbbZZWWyxxcqss85aFl544bLBBhuUE044YZrhI1o12ue9VPgb5f95/ZlnnikHHHBA1Yg888wzT3Xz5q677iq77bZbdRMh373oootW33H33XdP8x1pTPjgBz9Y3vOe91Tjtjdz3HHHVZ/fN+WfDs0111y9vys3PhpT9+dv1uV6661XTdc4RMYjjzxSPvGJT5RlllmmWvaFFlqobLvttuUPf/jDVPPPZ77//e9XzzNtPSRA3/U1kHVRN+x/6EMfmur7M896KIGs5+wDkZ7djUNK1EM8NO5fg92HGveN22+/vWy55Zblla98ZTVsxVve8pZy2223dbwtGAbqBDA+3XNP7t5O6THdiUx3/vlTPgcMinK8crxy/BR77bVXef/7399yCLN8f7JN7b777qWVfGcC+4499thyzwCuTb/5zW/K5ptvXmU9yvGUYS2/+MUvlv/+97/TTFtvq/vvv7984AMfKIssskiZccYZq3XWOATdNddcU62v7CcLLrhg+ehHP1qefvrpah4XXnhhWWeddap1kuPrU5/6VNNjkVGmzsNwUN4GGFdkMIJO/Oc/pRxzzJTnKVxL6QwAoy4Nkpdddlk5/vjjy9vf/vaqkT1uuOGG8oUvfKGstNJKVeNvX3ntl7/8ZXnf+95XNaj+4he/KF/72teqnqO/+tWvqgb/xmn322+/qtE1vUrTGJrhA/La1VdfXc4999yqMbVuMH3nO99ZDf3w7ne/u2oMf/jhh6vG5NNOO61q+B6qNC5nfvm9+Z40pMf1119f9WB+6qmnyhZbbFFWWGGFqpH79NNPLxdffHE1zMGyyy7bO58s849//OOqkTjLmt/01re+tff9rNM0Bq+66qpT3VQZiHq91M4+++zqJkiWL/N+4oknqtezjtLQnF63WZ70pM6NhXPOOadap5dccklZf/31q2lz8yKN71kHe++9d+8wG403OQa6Ln79619X+0FuTOVz+f5//etf5be//W35+te/XjXYZ7kyj9wU2XDDDae6EdJuqI+B7EO1fNfaa69dVl555eoGUtbNT3/606qx/k9/+lPVIN+pZ599tnrU6vXOIKgTMI7lXNXsmjkRbPvUU+Xogfbi7ekp+6y2Wjl7jjnKRJOAhYEELUAzyvHK8crxU3zve9+r3tt1112ree20005TZS5Kpql85qyzzmq5nJMmTaqyF+W4+PznP98byNWfbNPtt9++CsrK5xKYlW188MEHV9stQUMJOmr06KOPVts76yPrOkFzc889d+/+kO2XodqyLTJcW4L2vvOd71Tv5/jK9sg+m3lk//jKV75S5pxzzuqY75S6zfCXARd98cVy44MPVs/XOvPM8sBMM43i0jFeKG8zVqnbwCD1MCE8/vjjuXpXfxmEe++tEzNOeQ4AA+A63Lm77rqrWlfLLbdczxe/+MWmj4svvrh3+vvuu69n/vnn75lvvvmq50888UT12VlnnbXntttum2re+WzmPcsss0z13ksvvdSzww47VO999atf7X399ttv75k0aVLPQgst1HPPPff0vv7MM8/0rL/++tX0p556au/r733ve6vXbr311ml+1yOPPDLNb9xpp52aroO8t+GGG071Wv6f11dfffWeRx99dKr3nnvuuZ6ll166Z6655uq55ZZbpnrv6quv7plpppl6tthii6bf9ac//aln4YUX7pltttl6Lr300uq1448/vmeGGWboWWWVVXoefvjhpp+r1+WZZ5451etPPvlkz0orrVS99/3vf7967eSTT67+P+OMM/Zcdtll08xrl112qd7ff//9p3r9wgsvrF5ffvnle1588cXe17Pe8nrWY18DXRfZlosvvni1bI37Ve3ehnLfFVdcUX1vfnt/6yTTDXYfqveNPI444oip5n/AAQdUrx9++OE9A1EvV9+H89EgqBMwjrU6V0yEx8dL6Xnh5cEYOnq88H+fKxPw0eo6SHvjvU6gHK8cX1OOH1g5/oEHHuhZccUVq99Sr/sLLrig2t/zG7MszdTbJ/PNsbDWWmtV82g8RvJepsm0tZyD5plnnmmOtWyr973vfdX0Bx988FTfVf+2bPMXXnhhqvfq9ZvHeeedN9U2XXXVVat9coEFFui54YYbet/L8Z51m+M/03VK3ab7+q7TxRvKO4uPgXKHx/h4KG97jNWHus3QjPf6Da3JYAQAwJiTnp5JYd9Merym529kWIQMk5BezzvuuGOZPHly9dn0WE2v3WaS0r3xvfQUPeyww8qPfvSjqldteqTGGWecUaVsz/+XWGKJ3unTyzM9MzNEQKZPSvtGSZ3f1/zzz1+6IeskPUYbpWdresqmt+kaa6wx1XvpMZxeouedd17VuzM9TBslDf7ll19e9ZrNdEm9/81vfrPqNZ7e4QsssEC/y5PeyX/+85+r5//85z+rHr3/+Mc/yutf//qqV2ujzD/DAzR67rnnyplnnlmtnwwZ0egd73hH1Rs7vduTvj9DVLQz0HWR3sR1mv96n2qU/WkoBrsPpUf7Jz/5yaleS6/mQw89tHfIik7tv//+U/XGym9vXBaAyDkx19SJaNJTT5WZBjgESPryzzTvvGXxCdijum9ZAvpSjm9OOb5/E7kcn+HGku0n2ZV22WWXcsstt1SZf7IN83qG1Wsnx0KWa+ONNy6f+cxnykUXXdRy2qy7xx9/vBqGsPF4ypBnX/7yl6sMWfltyYbUaJZZZqnen6lFRpt6X6wlo1gyMP3ud7+rMhittdZave9lCLVkqUoGp/vuu683o1c76jbDXwZMBqPyfxmMFl1kkVJkMKILlLcZq9RtYHAEGAEAMOYkrfrPf/7zjqZNI+ZHPvKRctxxx/U2aO+1114tp2/WwL3UUktVDZN//OMfq8byNJ4mtX40ptGvJa17Usbfeuutva+lIT5p8pMSPyn0N9lkk+q72jXuD8Qb3vCGaV7LkBDxl7/8pRx44IHTvP/ggw+Wl156qfz1r3+tbhj0Vd+EyLJ+4xvfqBqw8/8FF1yw7fKk8TmPmH322avPZgiJ//mf/6nWYbtlz02NpNZPY3Q+31dez42JrOdObkwMdF1kGI5429veVobDQPeh2uqrr1418De7SfLvATbK5SZIHgD9mdCp4e+5J2P0TOkr3akZZijH3HZbOWbJJYdzyWC6pBzfnHJ8/yZ6OT7D8iVgLIFLCbJL4Fb+n2HiOpV1nmCrDCd31VVXVUPCDfS3LbnkktUwdFnPGXougUC1BAH1d0zktzf7Xe3eS2BbpwFG6jYjUAa8775S/i9oqwqKG2KwHlSUtwHGFQFGAABM97bccsveGxN77rlnv9MuvPDCLV9Pr9k0pKZBN70hW02fHqJ5Pb1ma9tss03Vq/boo4+uluXYY4+tpktD71FHHdW0UXWgmi3LY489Vv09/fTT+/3sU0891fK9m266qff3PvDAA+VPf/pTy/XUKL2W+/ZwbqXZ/Ppbx42NzvV07Qx0XaTnbgxX1o6B7kP99aCaNGlK1e3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",
      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. Figures saved to: E:\\불평등 연구\\데이터\\70_AI_정부책임\\_analysis_outputs_main_AIgov_figures_v1\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# 0) PATH\n",
    "# ============================================================\n",
    "DATA_DIR  = r\"E:\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")\n",
    "\n",
    "OUT_DIR = os.path.join(DATA_DIR, \"_analysis_outputs\")\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "# ============================================================\n",
    "# 1) Variables\n",
    "# ============================================================\n",
    "A = \"A_exposure\"\n",
    "B = \"B_protection\"\n",
    "\n",
    "DV1 = \"DV1_structural_attribution\"\n",
    "DV2 = \"DV2_public_responsibility\"\n",
    "DV3 = \"DV3_costly_policy_support\"\n",
    "\n",
    "NORM_INDEX_COL = \"Norm_collective_responsibility\"\n",
    "PREFERRED_NORM_ITEMS = [\"Norm1\", \"Norm2\", \"Norm3\"]  # (2 is reverse-coded)\n",
    "\n",
    "# ============================================================\n",
    "# 2) Pre-processing\n",
    "# ============================================================\n",
    "if \"ideology_0_10\" in df.columns:\n",
    "    df[\"ideology_0_10\"] = pd.to_numeric(df[\"ideology_0_10\"], errors=\"coerce\")\n",
    "    df[\"ideology_0_10\"] = df[\"ideology_0_10\"].round().clip(0, 10).astype(\"Int64\")\n",
    "\n",
    "if \"monthly_income_band\" not in df.columns:\n",
    "    if \"income_million_krw\" in df.columns:\n",
    "        df[\"monthly_income_band\"] = pd.qcut(\n",
    "            df[\"income_million_krw\"], q=8, labels=[f\"Q{i}\" for i in range(1, 9)]\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(\"monthly_income_band도 없고 income_million_krw도 없습니다. 소득 변수명을 확인하세요.\")\n",
    "\n",
    "def reverse_1to7(x):\n",
    "    return 8 - x\n",
    "\n",
    "if NORM_INDEX_COL in df.columns:\n",
    "    df[\"norm_index\"] = pd.to_numeric(df[NORM_INDEX_COL], errors=\"coerce\")\n",
    "else:\n",
    "    if all(c in df.columns for c in PREFERRED_NORM_ITEMS):\n",
    "        n1 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[0]], errors=\"coerce\")\n",
    "        n2 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[1]], errors=\"coerce\")\n",
    "        n3 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[2]], errors=\"coerce\")\n",
    "        df[\"norm_index\"] = pd.concat([n1, reverse_1to7(n2), n3], axis=1).mean(axis=1)\n",
    "    else:\n",
    "        raise ValueError(\n",
    "            f\"Norm index를 만들 수 없습니다. {NORM_INDEX_COL}가 없고, \"\n",
    "            f\"{PREFERRED_NORM_ITEMS}도 없습니다. df.xlsx의 Norm 문항 변수명을 확인하세요.\"\n",
    "        )\n",
    "\n",
    "df[\"norm_c\"] = (df[\"norm_index\"] - df[\"norm_index\"].mean()).fillna(0.0)\n",
    "\n",
    "# ============================================================\n",
    "# 3) Controls\n",
    "# ============================================================\n",
    "CAT = pb.C\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_income_band)\",\n",
    "    \"CAT(homeownership)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(employment)\",\n",
    "    \"CAT(occupation)\",\n",
    "    \"ideology_0_10\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "# ============================================================\n",
    "# 4) Helpers\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def short_term_name(term):\n",
    "    # map variable names to reader-friendly labels\n",
    "    out = term\n",
    "    out = out.replace(A, \"Exposure\")\n",
    "    out = out.replace(B, \"Protection\")\n",
    "    out = out.replace(\"norm_c\", \"Norm\")\n",
    "    out = out.replace(\":\", \"×\")\n",
    "    return out\n",
    "\n",
    "def extract_terms(model, term_list):\n",
    "    rows = []\n",
    "    for t in term_list:\n",
    "        if t in model.params.index:\n",
    "            rows.append((t,\n",
    "                         float(model.params[t]),\n",
    "                         float(model.bse[t]),\n",
    "                         float(model.pvalues[t])))\n",
    "    return rows\n",
    "\n",
    "def coefpanel(ax, rows, panel_title, xlab=\"Coefficient (95% CI)\", panel_tag=\"\"):\n",
    "    if not rows:\n",
    "        ax.axis(\"off\")\n",
    "        return\n",
    "\n",
    "    y = np.arange(len(rows))\n",
    "    coefs = np.array([r[1] for r in rows])\n",
    "    ses   = np.array([r[2] for r in rows])\n",
    "\n",
    "    ci_low  = coefs - 1.96*ses\n",
    "    ci_high = coefs + 1.96*ses\n",
    "\n",
    "    ax.hlines(y, ci_low, ci_high, color=\"black\", linewidth=2)\n",
    "    ax.plot(coefs, y, \"o\", color=\"red\", markersize=9)\n",
    "    ax.axvline(0, color=\"red\", linestyle=\"--\", linewidth=1.6)\n",
    "\n",
    "    labels = [short_term_name(r[0]) for r in rows]\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(labels, fontsize=24)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    xmin, xmax = min(ci_low.min(), 0), max(ci_high.max(), 0)\n",
    "    span = xmax - xmin if xmax > xmin else 1.0\n",
    "    ax.set_xlim(xmin - 0.25*span, xmax + 0.25*span)\n",
    "\n",
    "    dx = 0.04 * span\n",
    "    for i, (t, coef, se, p) in enumerate(rows):\n",
    "        txt = f\"{coef:.3f}, p={p:.3f}\"\n",
    "        x_text = coef + (dx if coef >= 0 else -dx)\n",
    "        ax.annotate(txt, xy=(x_text, i), xytext=(0, 3),\n",
    "                    textcoords=\"offset points\", ha=\"left\", va=\"bottom\",\n",
    "                    fontsize=16)\n",
    "\n",
    "    ax.set_title(f\"{panel_tag} {panel_title}\", fontsize=22, pad=12)\n",
    "    ax.tick_params(axis=\"x\", labelsize=14, pad=6)\n",
    "    ax.tick_params(axis=\"y\", labelsize=14, pad=7)\n",
    "    ax.set_xlabel(xlab, fontsize=14, labelpad=10)\n",
    "\n",
    "# ============================================================\n",
    "# 5) FIGURE 1 — DV1 (1×2 panels)\n",
    "# ============================================================\n",
    "dv = DV1\n",
    "\n",
    "m_a = fit_ols(f\"{dv} ~ {A} + {B} + {CONTROLS}\", df)\n",
    "rows_a = extract_terms(m_a, [A, B])\n",
    "\n",
    "m_b = fit_ols(f\"{dv} ~ {A}*{B} + {CONTROLS}\", df)\n",
    "rows_b = extract_terms(m_b, [f\"{A}:{B}\"])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6.2), constrained_layout=True)\n",
    "fig.subplots_adjust(wspace=0.55)\n",
    "\n",
    "coefpanel(axes[0], rows_a, \"Main effects: Exposure, Protection\", \"Coefficient (95% CI)\", \"(a)\")\n",
    "coefpanel(axes[1], rows_b, \"Translation: Exposure × Protection\", \"Coefficient (95% CI)\", \"(b)\")\n",
    "\n",
    "outpath = os.path.join(OUT_DIR, \"Figure1_DV1_structural_attribution_1row2panels.png\")\n",
    "plt.savefig(outpath, dpi=600, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# ============================================================\n",
    "# 6) FIGURE 2 — DV2 (1×3 panels)\n",
    "# ============================================================\n",
    "dv = DV2\n",
    "\n",
    "m_a = fit_ols(f\"{dv} ~ {A} + {B} + {CONTROLS}\", df)\n",
    "rows_a = extract_terms(m_a, [A, B])\n",
    "\n",
    "m_b = fit_ols(f\"{dv} ~ {A}*{B} + {CONTROLS}\", df)\n",
    "rows_b = extract_terms(m_b, [f\"{A}:{B}\"])\n",
    "\n",
    "m_c = fit_ols(f\"{dv} ~ {A}*{B}*norm_c + {CONTROLS}\", df)\n",
    "rows_c = extract_terms(m_c, [f\"{A}:{B}:norm_c\"])\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(22, 6.2), constrained_layout=True)\n",
    "fig.subplots_adjust(wspace=0.55)\n",
    "\n",
    "coefpanel(axes[0], rows_a, \"Main effects: Exposure, Protection\", \"Coefficient (95% CI)\", \"(a)\")\n",
    "coefpanel(axes[1], rows_b, \"Translation: Exp. × Prot.\", \"Coefficient (95% CI)\", \"(b)\")\n",
    "coefpanel(axes[2], rows_c, \"Conditional translation: Exp. × Prot. × Norm\", \"Coefficient (95% CI)\", \"(c)\")\n",
    "\n",
    "outpath = os.path.join(OUT_DIR, \"Figure2_DV2_public_responsibility_1row3panels.png\")\n",
    "plt.savefig(outpath, dpi=600, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# ============================================================\n",
    "# 7) FIGURE 3 — DV3 (1×3 panels)\n",
    "# ============================================================\n",
    "dv = DV3\n",
    "\n",
    "m_a = fit_ols(f\"{dv} ~ {A} + {B} + {CONTROLS}\", df)\n",
    "rows_a = extract_terms(m_a, [A, B])\n",
    "\n",
    "m_b = fit_ols(f\"{dv} ~ {A}*{B} + {CONTROLS}\", df)\n",
    "rows_b = extract_terms(m_b, [f\"{A}:{B}\"])\n",
    "\n",
    "m_c = fit_ols(f\"{dv} ~ {A}*{B}*norm_c + {CONTROLS}\", df)\n",
    "rows_c = extract_terms(m_c, [f\"{A}:{B}:norm_c\"])\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(22, 6.2), constrained_layout=True)\n",
    "fig.subplots_adjust(wspace=0.55)\n",
    "\n",
    "coefpanel(axes[0], rows_a, \"Main effects: Exposure, Protection\", \"Coefficient (95% CI)\", \"(a)\")\n",
    "coefpanel(axes[1], rows_b, \"Translation: Exp. × Prot.\", \"Coefficient (95% CI)\", \"(b)\")\n",
    "coefpanel(axes[2], rows_c, \"Conditional ranslation: Exp. × Prot. × Norm\", \"Coefficient (95% CI)\", \"(c)\")\n",
    "\n",
    "outpath = os.path.join(OUT_DIR, \"Figure3_DV3_costly_policy_support_1row3panels.png\")\n",
    "plt.savefig(outpath, dpi=600, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "print(\"Done. Figures saved to:\", OUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3cb1b7c-1c01-4fd5-8959-f416f7fbce22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7475b866-f8b6-4f5c-aeb7-064de8c452f5",
   "metadata": {},
   "source": [
    "## 2. Full results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "798dad61-1976-4e21-8cef-5558088184ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Figure 1 (DV1: Structural attribution) — **Recognition stage (mechanism)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (Figure panel a): DV ~ A + B + controls**  \n",
       "N = 3,000 | R² = 0.288 | Adj. R² = 0.281"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>1.121</td>\n",
       "      <td>0.033</td>\n",
       "      <td>33.646</td>\n",
       "      <td>3.54e-248</td>\n",
       "      <td>***</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.033</td>\n",
       "      <td>2.998</td>\n",
       "      <td>0.00272</td>\n",
       "      <td>***</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            term   coef  se_HC3       t          p  sig  ci_low  ci_high\n",
       "27    A_exposure  1.121   0.033  33.646  3.54e-248  ***   1.055    1.186\n",
       "28  B_protection  0.099   0.033   2.998    0.00272  ***   0.034    0.164"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 3,000 | R² = 0.288 | Adj. R² = 0.281"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.561</td>\n",
       "      <td>0.103</td>\n",
       "      <td>34.527</td>\n",
       "      <td>3.16e-261</td>\n",
       "      <td>***</td>\n",
       "      <td>3.359</td>\n",
       "      <td>3.763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.638</td>\n",
       "      <td>0.523</td>\n",
       "      <td></td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.628</td>\n",
       "      <td></td>\n",
       "      <td>-0.239</td>\n",
       "      <td>0.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.951</td>\n",
       "      <td></td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.638</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-1.669</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>0.432</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.319</td>\n",
       "      <td>0.75</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.714</td>\n",
       "      <td></td>\n",
       "      <td>-0.106</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-1.801</td>\n",
       "      <td>0.0718</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.248</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.063</td>\n",
       "      <td>-0.352</td>\n",
       "      <td>0.725</td>\n",
       "      <td></td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.372</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-2.018</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.259</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.763</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.007</td>\n",
       "      <td>0.314</td>\n",
       "      <td></td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-0.976</td>\n",
       "      <td>0.329</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.98</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.076</td>\n",
       "      <td>2.036</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>**</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.4</td>\n",
       "      <td></td>\n",
       "      <td>-0.160</td>\n",
       "      <td>0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.043</td>\n",
       "      <td>2.252</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>**</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.464</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.075</td>\n",
       "      <td>2.241</td>\n",
       "      <td>0.025</td>\n",
       "      <td>**</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>-0.142</td>\n",
       "      <td>0.128</td>\n",
       "      <td>-1.112</td>\n",
       "      <td>0.266</td>\n",
       "      <td></td>\n",
       "      <td>-0.393</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.941</td>\n",
       "      <td>0.347</td>\n",
       "      <td></td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.366</td>\n",
       "      <td></td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.718</td>\n",
       "      <td></td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.951</td>\n",
       "      <td></td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>1.121</td>\n",
       "      <td>0.033</td>\n",
       "      <td>33.646</td>\n",
       "      <td>3.54e-248</td>\n",
       "      <td>***</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.033</td>\n",
       "      <td>2.998</td>\n",
       "      <td>0.00272</td>\n",
       "      <td>***</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.129</td>\n",
       "      <td>0.259</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.401</td>\n",
       "      <td></td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.561   0.103  34.527   \n",
       "1                                 CAT(gender)[T.Male] -0.021   0.033  -0.638   \n",
       "2                           CAT(gender)[T.Other/PNTS]  0.078   0.162   0.484   \n",
       "3                   CAT(region)[T.Non-capital region]  0.002   0.033   0.062   \n",
       "4                         CAT(education)[T.Graduate+]  0.027   0.057   0.470   \n",
       "5               CAT(education)[T.High school or less] -0.077   0.046  -1.669   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.032   0.041  -0.786   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.020   0.064  -0.319   \n",
       "8                      CAT(monthly_income_band)[T.Q3]  0.025   0.067   0.367   \n",
       "9                      CAT(monthly_income_band)[T.Q4] -0.119   0.066  -1.801   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.022   0.063  -0.352   \n",
       "11                     CAT(monthly_income_band)[T.Q6]  0.057   0.063   0.893   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.131   0.065  -2.018   \n",
       "13                     CAT(monthly_income_band)[T.Q8]  0.020   0.065   0.302   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.034   0.033   1.007   \n",
       "15               CAT(marital_status)[T.Never married] -0.036   0.037  -0.976   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.001   0.058  -0.024   \n",
       "17              CAT(employment)[T.Not in labor force]  0.154   0.076   2.036   \n",
       "18                           CAT(employment)[T.Other]  0.121   0.143   0.842   \n",
       "19                       CAT(employment)[T.Permanent]  0.096   0.043   2.252   \n",
       "20                   CAT(employment)[T.Self-employed]  0.041   0.056   0.731   \n",
       "21              CAT(employment)[T.Unemployed/seeking]  0.168   0.075   2.241   \n",
       "22                        CAT(occupation)[T.Other/NA] -0.142   0.128  -1.112   \n",
       "23              CAT(occupation)[T.Production/On-site] -0.050   0.053  -0.941   \n",
       "24         CAT(occupation)[T.Professional/Managerial]  0.045   0.049   0.904   \n",
       "25                   CAT(occupation)[T.Public sector] -0.026   0.071  -0.361   \n",
       "26                         CAT(occupation)[T.Service]  0.003   0.051   0.061   \n",
       "27                                         A_exposure  1.121   0.033  33.646   \n",
       "28                                       B_protection  0.099   0.033   2.998   \n",
       "29                                                age -0.001   0.001  -1.129   \n",
       "30                                      ideology_0_10  0.007   0.008   0.840   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   3.16e-261  ***   3.359    3.763  \n",
       "1       0.523       -0.087    0.044  \n",
       "2       0.628       -0.239    0.395  \n",
       "3       0.951       -0.063    0.067  \n",
       "4       0.638       -0.085    0.138  \n",
       "5      0.0951    *  -0.167    0.013  \n",
       "6       0.432       -0.113    0.048  \n",
       "7        0.75       -0.145    0.105  \n",
       "8       0.714       -0.106    0.155  \n",
       "9      0.0718    *  -0.248    0.010  \n",
       "10      0.725       -0.146    0.101  \n",
       "11      0.372       -0.068    0.181  \n",
       "12     0.0436   **  -0.259   -0.004  \n",
       "13      0.763       -0.109    0.148  \n",
       "14      0.314       -0.032    0.099  \n",
       "15      0.329       -0.109    0.037  \n",
       "16       0.98       -0.114    0.111  \n",
       "17     0.0418   **   0.006    0.303  \n",
       "18        0.4       -0.160    0.402  \n",
       "19     0.0243   **   0.012    0.180  \n",
       "20      0.464       -0.068    0.149  \n",
       "21      0.025   **   0.021    0.315  \n",
       "22      0.266       -0.393    0.109  \n",
       "23      0.347       -0.155    0.054  \n",
       "24      0.366       -0.052    0.141  \n",
       "25      0.718       -0.164    0.113  \n",
       "26      0.951       -0.097    0.104  \n",
       "27  3.54e-248  ***   1.055    1.186  \n",
       "28    0.00272  ***   0.034    0.164  \n",
       "29      0.259       -0.004    0.001  \n",
       "30      0.401       -0.009    0.022  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Figure panel b): DV ~ A×B + controls**  \n",
       "N = 3,000 | R² = 0.288 | Adj. R² = 0.281"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>0.708</td>\n",
       "      <td></td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       term   coef  se_HC3      t      p sig  ci_low  ci_high\n",
       "29  A_exposure:B_protection -0.025   0.066 -0.374  0.708      -0.155    0.105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 3,000 | R² = 0.288 | Adj. R² = 0.281"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.555</td>\n",
       "      <td>0.104</td>\n",
       "      <td>34.136</td>\n",
       "      <td>2.17e-255</td>\n",
       "      <td>***</td>\n",
       "      <td>3.351</td>\n",
       "      <td>3.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.627</td>\n",
       "      <td>0.531</td>\n",
       "      <td></td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.63</td>\n",
       "      <td></td>\n",
       "      <td>-0.239</td>\n",
       "      <td>0.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.95</td>\n",
       "      <td></td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.637</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-1.665</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.784</td>\n",
       "      <td>0.433</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.310</td>\n",
       "      <td>0.757</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.715</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-1.802</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.248</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.063</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>0.726</td>\n",
       "      <td></td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.37</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-2.012</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.259</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.753</td>\n",
       "      <td></td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.001</td>\n",
       "      <td>0.317</td>\n",
       "      <td></td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-0.977</td>\n",
       "      <td>0.329</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.976</td>\n",
       "      <td></td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.076</td>\n",
       "      <td>2.035</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>**</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.405</td>\n",
       "      <td></td>\n",
       "      <td>-0.162</td>\n",
       "      <td>0.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.043</td>\n",
       "      <td>2.241</td>\n",
       "      <td>0.025</td>\n",
       "      <td>**</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.472</td>\n",
       "      <td></td>\n",
       "      <td>-0.069</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.075</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0256</td>\n",
       "      <td>**</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.128</td>\n",
       "      <td>-1.118</td>\n",
       "      <td>0.264</td>\n",
       "      <td></td>\n",
       "      <td>-0.394</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.942</td>\n",
       "      <td>0.346</td>\n",
       "      <td></td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.365</td>\n",
       "      <td></td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.718</td>\n",
       "      <td></td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.961</td>\n",
       "      <td></td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>1.134</td>\n",
       "      <td>0.047</td>\n",
       "      <td>24.040</td>\n",
       "      <td>1.05e-127</td>\n",
       "      <td>***</td>\n",
       "      <td>1.041</td>\n",
       "      <td>1.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.047</td>\n",
       "      <td>2.396</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>**</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>0.708</td>\n",
       "      <td></td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.133</td>\n",
       "      <td>0.257</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.402</td>\n",
       "      <td></td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.555   0.104  34.136   \n",
       "1                                 CAT(gender)[T.Male] -0.021   0.033  -0.627   \n",
       "2                           CAT(gender)[T.Other/PNTS]  0.078   0.162   0.482   \n",
       "3                   CAT(region)[T.Non-capital region]  0.002   0.033   0.062   \n",
       "4                         CAT(education)[T.Graduate+]  0.027   0.057   0.472   \n",
       "5               CAT(education)[T.High school or less] -0.077   0.046  -1.665   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.032   0.041  -0.784   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.020   0.064  -0.310   \n",
       "8                      CAT(monthly_income_band)[T.Q3]  0.024   0.067   0.365   \n",
       "9                      CAT(monthly_income_band)[T.Q4] -0.119   0.066  -1.802   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.022   0.063  -0.350   \n",
       "11                     CAT(monthly_income_band)[T.Q6]  0.057   0.064   0.897   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.131   0.065  -2.012   \n",
       "13                     CAT(monthly_income_band)[T.Q8]  0.021   0.066   0.314   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.033   0.033   1.001   \n",
       "15               CAT(marital_status)[T.Never married] -0.036   0.037  -0.977   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.002   0.058  -0.030   \n",
       "17              CAT(employment)[T.Not in labor force]  0.154   0.076   2.035   \n",
       "18                           CAT(employment)[T.Other]  0.119   0.144   0.832   \n",
       "19                       CAT(employment)[T.Permanent]  0.096   0.043   2.241   \n",
       "20                   CAT(employment)[T.Self-employed]  0.040   0.056   0.719   \n",
       "21              CAT(employment)[T.Unemployed/seeking]  0.167   0.075   2.232   \n",
       "22                        CAT(occupation)[T.Other/NA] -0.143   0.128  -1.118   \n",
       "23              CAT(occupation)[T.Production/On-site] -0.050   0.053  -0.942   \n",
       "24         CAT(occupation)[T.Professional/Managerial]  0.045   0.049   0.905   \n",
       "25                   CAT(occupation)[T.Public sector] -0.025   0.071  -0.361   \n",
       "26                         CAT(occupation)[T.Service]  0.003   0.051   0.049   \n",
       "27                                         A_exposure  1.134   0.047  24.040   \n",
       "28                                       B_protection  0.112   0.047   2.396   \n",
       "29                            A_exposure:B_protection -0.025   0.066  -0.374   \n",
       "30                                                age -0.001   0.001  -1.133   \n",
       "31                                      ideology_0_10  0.007   0.008   0.838   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   2.17e-255  ***   3.351    3.759  \n",
       "1       0.531       -0.086    0.044  \n",
       "2        0.63       -0.239    0.395  \n",
       "3        0.95       -0.063    0.067  \n",
       "4       0.637       -0.085    0.138  \n",
       "5      0.0958    *  -0.167    0.014  \n",
       "6       0.433       -0.113    0.049  \n",
       "7       0.757       -0.145    0.105  \n",
       "8       0.715       -0.107    0.155  \n",
       "9      0.0716    *  -0.248    0.010  \n",
       "10      0.726       -0.146    0.102  \n",
       "11       0.37       -0.068    0.182  \n",
       "12     0.0442   **  -0.259   -0.003  \n",
       "13      0.753       -0.108    0.149  \n",
       "14      0.317       -0.032    0.099  \n",
       "15      0.329       -0.109    0.037  \n",
       "16      0.976       -0.115    0.111  \n",
       "17     0.0418   **   0.006    0.303  \n",
       "18      0.405       -0.162    0.401  \n",
       "19      0.025   **   0.012    0.180  \n",
       "20      0.472       -0.069    0.149  \n",
       "21     0.0256   **   0.020    0.314  \n",
       "22      0.264       -0.394    0.108  \n",
       "23      0.346       -0.155    0.054  \n",
       "24      0.365       -0.052    0.141  \n",
       "25      0.718       -0.164    0.113  \n",
       "26      0.961       -0.098    0.103  \n",
       "27  1.05e-127  ***   1.041    1.226  \n",
       "28     0.0166   **   0.020    0.203  \n",
       "29      0.708       -0.155    0.105  \n",
       "30      0.257       -0.004    0.001  \n",
       "31      0.402       -0.009    0.022  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 2 (DV2: Public responsibility) — **H1/H2 core: Translation into public responsibility**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (Figure panel a): DV ~ A + B + controls**  \n",
       "N = 3,000 | R² = 0.115 | Adj. R² = 0.106"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.039</td>\n",
       "      <td>10.581</td>\n",
       "      <td>3.64e-26</td>\n",
       "      <td>***</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.039</td>\n",
       "      <td>13.246</td>\n",
       "      <td>4.79e-40</td>\n",
       "      <td>***</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            term   coef  se_HC3       t         p  sig  ci_low  ci_high\n",
       "27    A_exposure  0.414   0.039  10.581  3.64e-26  ***   0.337    0.490\n",
       "28  B_protection  0.521   0.039  13.246  4.79e-40  ***   0.444    0.598"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 3,000 | R² = 0.115 | Adj. R² = 0.106"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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    },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.144</td>\n",
       "      <td>0.123</td>\n",
       "      <td>33.758</td>\n",
       "      <td>8.07e-250</td>\n",
       "      <td>***</td>\n",
       "      <td>3.904</td>\n",
       "      <td>4.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-2.243</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.165</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.214</td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.835</td>\n",
       "      <td></td>\n",
       "      <td>-0.463</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.177</td>\n",
       "      <td>0.239</td>\n",
       "      <td></td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.784</td>\n",
       "      <td>0.433</td>\n",
       "      <td></td>\n",
       "      <td>-0.179</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.858</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.471</td>\n",
       "      <td>0.638</td>\n",
       "      <td></td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.634</td>\n",
       "      <td></td>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.582</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.439</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.121</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-1.552</td>\n",
       "      <td>0.121</td>\n",
       "      <td></td>\n",
       "      <td>-0.274</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>0.473</td>\n",
       "      <td></td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>0.413</td>\n",
       "      <td></td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.052</td>\n",
       "      <td>0.293</td>\n",
       "      <td></td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.039</td>\n",
       "      <td>1.251</td>\n",
       "      <td>0.211</td>\n",
       "      <td></td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.915</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.956</td>\n",
       "      <td>0.339</td>\n",
       "      <td></td>\n",
       "      <td>-0.187</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.456</td>\n",
       "      <td></td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.184</td>\n",
       "      <td>-0.552</td>\n",
       "      <td>0.581</td>\n",
       "      <td></td>\n",
       "      <td>-0.462</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.557</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.975</td>\n",
       "      <td></td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.984</td>\n",
       "      <td></td>\n",
       "      <td>-0.187</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>-0.332</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-2.196</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.629</td>\n",
       "      <td>-0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.867</td>\n",
       "      <td>0.386</td>\n",
       "      <td></td>\n",
       "      <td>-0.174</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-1.453</td>\n",
       "      <td>0.146</td>\n",
       "      <td></td>\n",
       "      <td>-0.199</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-1.405</td>\n",
       "      <td>0.16</td>\n",
       "      <td></td>\n",
       "      <td>-0.280</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-2.871</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>-0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.039</td>\n",
       "      <td>10.581</td>\n",
       "      <td>3.64e-26</td>\n",
       "      <td>***</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.039</td>\n",
       "      <td>13.246</td>\n",
       "      <td>4.79e-40</td>\n",
       "      <td>***</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>0.325</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-6.770</td>\n",
       "      <td>1.29e-11</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>-0.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  4.144   0.123  33.758   \n",
       "1                                 CAT(gender)[T.Male] -0.088   0.039  -2.243   \n",
       "2                           CAT(gender)[T.Other/PNTS] -0.044   0.214  -0.208   \n",
       "3                   CAT(region)[T.Non-capital region] -0.046   0.039  -1.177   \n",
       "4                         CAT(education)[T.Graduate+] -0.051   0.065  -0.784   \n",
       "5               CAT(education)[T.High school or less]  0.010   0.054   0.179   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.022   0.048  -0.471   \n",
       "7                      CAT(monthly_income_band)[T.Q2]  0.036   0.076   0.477   \n",
       "8                      CAT(monthly_income_band)[T.Q3]  0.042   0.076   0.551   \n",
       "9                      CAT(monthly_income_band)[T.Q4]  0.060   0.078   0.774   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.121   0.078  -1.552   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.054   0.076  -0.717   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.064   0.078  -0.818   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.081   0.077  -1.052   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.049   0.039   1.251   \n",
       "15               CAT(marital_status)[T.Never married]  0.005   0.045   0.106   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.061   0.064  -0.956   \n",
       "17              CAT(employment)[T.Not in labor force]  0.067   0.090   0.746   \n",
       "18                           CAT(employment)[T.Other] -0.102   0.184  -0.552   \n",
       "19                       CAT(employment)[T.Permanent]  0.029   0.049   0.587   \n",
       "20                   CAT(employment)[T.Self-employed]  0.002   0.065   0.031   \n",
       "21              CAT(employment)[T.Unemployed/seeking]  0.002   0.097   0.020   \n",
       "22                        CAT(occupation)[T.Other/NA] -0.332   0.151  -2.196   \n",
       "23              CAT(occupation)[T.Production/On-site] -0.053   0.062  -0.867   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.085   0.058  -1.453   \n",
       "25                   CAT(occupation)[T.Public sector] -0.117   0.083  -1.405   \n",
       "26                         CAT(occupation)[T.Service] -0.176   0.061  -2.871   \n",
       "27                                         A_exposure  0.414   0.039  10.581   \n",
       "28                                       B_protection  0.521   0.039  13.246   \n",
       "29                                                age -0.002   0.002  -0.985   \n",
       "30                                      ideology_0_10 -0.061   0.009  -6.770   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   8.07e-250  ***   3.904    4.385  \n",
       "1      0.0249   **  -0.165   -0.011  \n",
       "2       0.835       -0.463    0.375  \n",
       "3       0.239       -0.123    0.031  \n",
       "4       0.433       -0.179    0.077  \n",
       "5       0.858       -0.096    0.116  \n",
       "6       0.638       -0.116    0.071  \n",
       "7       0.634       -0.113    0.185  \n",
       "8       0.582       -0.107    0.190  \n",
       "9       0.439       -0.092    0.212  \n",
       "10      0.121       -0.274    0.032  \n",
       "11      0.473       -0.203    0.094  \n",
       "12      0.413       -0.218    0.090  \n",
       "13      0.293       -0.232    0.070  \n",
       "14      0.211       -0.028    0.126  \n",
       "15      0.915       -0.083    0.092  \n",
       "16      0.339       -0.187    0.064  \n",
       "17      0.456       -0.110    0.244  \n",
       "18      0.581       -0.462    0.259  \n",
       "19      0.557       -0.068    0.126  \n",
       "20      0.975       -0.125    0.129  \n",
       "21      0.984       -0.187    0.191  \n",
       "22     0.0281   **  -0.629   -0.036  \n",
       "23      0.386       -0.174    0.067  \n",
       "24      0.146       -0.199    0.030  \n",
       "25       0.16       -0.280    0.046  \n",
       "26     0.0041  ***  -0.296   -0.056  \n",
       "27   3.64e-26  ***   0.337    0.490  \n",
       "28   4.79e-40  ***   0.444    0.598  \n",
       "29      0.325       -0.005    0.001  \n",
       "30   1.29e-11  ***  -0.078   -0.043  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Figure panel b): DV ~ A×B + controls**  \n",
       "N = 3,000 | R² = 0.140 | Adj. R² = 0.131"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.077</td>\n",
       "      <td>9.242</td>\n",
       "      <td>2.42e-20</td>\n",
       "      <td>***</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       term   coef  se_HC3      t         p  sig  ci_low  \\\n",
       "29  A_exposure:B_protection  0.712   0.077  9.242  2.42e-20  ***   0.561   \n",
       "\n",
       "    ci_high  \n",
       "29    0.863  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 3,000 | R² = 0.140 | Adj. R² = 0.131"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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    },
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.318</td>\n",
       "      <td>0.121</td>\n",
       "      <td>35.564</td>\n",
       "      <td>4.98e-277</td>\n",
       "      <td>***</td>\n",
       "      <td>4.080</td>\n",
       "      <td>4.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-2.534</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.174</td>\n",
       "      <td>-0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.208</td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.861</td>\n",
       "      <td></td>\n",
       "      <td>-0.445</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.206</td>\n",
       "      <td>0.228</td>\n",
       "      <td></td>\n",
       "      <td>-0.122</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.848</td>\n",
       "      <td>0.396</td>\n",
       "      <td></td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.927</td>\n",
       "      <td></td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.529</td>\n",
       "      <td>0.597</td>\n",
       "      <td></td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.795</td>\n",
       "      <td></td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.558</td>\n",
       "      <td></td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.414</td>\n",
       "      <td></td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-1.634</td>\n",
       "      <td>0.102</td>\n",
       "      <td></td>\n",
       "      <td>-0.275</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.837</td>\n",
       "      <td>0.402</td>\n",
       "      <td></td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.952</td>\n",
       "      <td>0.341</td>\n",
       "      <td></td>\n",
       "      <td>-0.226</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-1.390</td>\n",
       "      <td>0.165</td>\n",
       "      <td></td>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.039</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.158</td>\n",
       "      <td></td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.896</td>\n",
       "      <td></td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.063</td>\n",
       "      <td>-0.822</td>\n",
       "      <td>0.411</td>\n",
       "      <td></td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.439</td>\n",
       "      <td></td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.183</td>\n",
       "      <td>-0.346</td>\n",
       "      <td>0.729</td>\n",
       "      <td></td>\n",
       "      <td>-0.421</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.441</td>\n",
       "      <td></td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.771</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.823</td>\n",
       "      <td></td>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>0.146</td>\n",
       "      <td>-2.166</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.603</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.851</td>\n",
       "      <td>0.395</td>\n",
       "      <td></td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.508</td>\n",
       "      <td>0.131</td>\n",
       "      <td></td>\n",
       "      <td>-0.198</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-1.424</td>\n",
       "      <td>0.154</td>\n",
       "      <td></td>\n",
       "      <td>-0.280</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-2.631</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.276</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.426</td>\n",
       "      <td></td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.052</td>\n",
       "      <td>3.156</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>***</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.077</td>\n",
       "      <td>9.242</td>\n",
       "      <td>2.42e-20</td>\n",
       "      <td>***</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.878</td>\n",
       "      <td>0.38</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-6.811</td>\n",
       "      <td>9.7e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>-0.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  4.318   0.121  35.564   \n",
       "1                                 CAT(gender)[T.Male] -0.098   0.039  -2.534   \n",
       "2                           CAT(gender)[T.Other/PNTS] -0.036   0.208  -0.175   \n",
       "3                   CAT(region)[T.Non-capital region] -0.047   0.039  -1.206   \n",
       "4                         CAT(education)[T.Graduate+] -0.054   0.064  -0.848   \n",
       "5               CAT(education)[T.High school or less]  0.005   0.053   0.091   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.025   0.047  -0.529   \n",
       "7                      CAT(monthly_income_band)[T.Q2]  0.020   0.075   0.260   \n",
       "8                      CAT(monthly_income_band)[T.Q3]  0.044   0.074   0.586   \n",
       "9                      CAT(monthly_income_band)[T.Q4]  0.062   0.076   0.818   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.125   0.076  -1.634   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.063   0.075  -0.837   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.074   0.078  -0.952   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.105   0.076  -1.390   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.055   0.039   1.410   \n",
       "15               CAT(marital_status)[T.Never married]  0.006   0.044   0.130   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.052   0.063  -0.822   \n",
       "17              CAT(employment)[T.Not in labor force]  0.068   0.088   0.775   \n",
       "18                           CAT(employment)[T.Other] -0.063   0.183  -0.346   \n",
       "19                       CAT(employment)[T.Permanent]  0.037   0.049   0.770   \n",
       "20                   CAT(employment)[T.Self-employed]  0.019   0.064   0.291   \n",
       "21              CAT(employment)[T.Unemployed/seeking]  0.021   0.094   0.224   \n",
       "22                        CAT(occupation)[T.Other/NA] -0.317   0.146  -2.166   \n",
       "23              CAT(occupation)[T.Production/On-site] -0.052   0.061  -0.851   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.086   0.057  -1.508   \n",
       "25                   CAT(occupation)[T.Public sector] -0.118   0.083  -1.424   \n",
       "26                         CAT(occupation)[T.Service] -0.158   0.060  -2.631   \n",
       "27                                         A_exposure  0.042   0.053   0.796   \n",
       "28                                       B_protection  0.165   0.052   3.156   \n",
       "29                            A_exposure:B_protection  0.712   0.077   9.242   \n",
       "30                                                age -0.001   0.002  -0.878   \n",
       "31                                      ideology_0_10 -0.060   0.009  -6.811   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   4.98e-277  ***   4.080    4.556  \n",
       "1      0.0113   **  -0.174   -0.022  \n",
       "2       0.861       -0.445    0.372  \n",
       "3       0.228       -0.122    0.029  \n",
       "4       0.396       -0.180    0.071  \n",
       "5       0.927       -0.100    0.110  \n",
       "6       0.597       -0.117    0.067  \n",
       "7       0.795       -0.128    0.167  \n",
       "8       0.558       -0.102    0.190  \n",
       "9       0.414       -0.087    0.212  \n",
       "10      0.102       -0.275    0.025  \n",
       "11      0.402       -0.210    0.084  \n",
       "12      0.341       -0.226    0.078  \n",
       "13      0.165       -0.254    0.043  \n",
       "14      0.158       -0.021    0.131  \n",
       "15      0.896       -0.080    0.092  \n",
       "16      0.411       -0.176    0.072  \n",
       "17      0.439       -0.104    0.240  \n",
       "18      0.729       -0.421    0.295  \n",
       "19      0.441       -0.058    0.133  \n",
       "20      0.771       -0.107    0.145  \n",
       "21      0.823       -0.163    0.206  \n",
       "22     0.0303   **  -0.603   -0.030  \n",
       "23      0.395       -0.170    0.067  \n",
       "24      0.131       -0.198    0.026  \n",
       "25      0.154       -0.280    0.044  \n",
       "26     0.0085  ***  -0.276   -0.040  \n",
       "27      0.426       -0.062    0.147  \n",
       "28     0.0016  ***   0.062    0.267  \n",
       "29   2.42e-20  ***   0.561    0.863  \n",
       "30       0.38       -0.004    0.002  \n",
       "31    9.7e-12  ***  -0.078   -0.043  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Figure panel c): DV ~ A×B×Norm + controls**  \n",
       "N = 3,000 | R² = 0.314 | Adj. R² = 0.306"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A_exposure:B_protection:norm_c</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.073</td>\n",
       "      <td>4.993</td>\n",
       "      <td>5.96e-07</td>\n",
       "      <td>***</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                              term   coef  se_HC3      t         p  sig  \\\n",
       "33  A_exposure:B_protection:norm_c  0.366   0.073  4.993  5.96e-07  ***   \n",
       "\n",
       "    ci_low  ci_high  \n",
       "33   0.222    0.509  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 3,000 | R² = 0.314 | Adj. R² = 0.306"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.834</td>\n",
       "      <td>0.113</td>\n",
       "      <td>33.916</td>\n",
       "      <td>3.89e-252</td>\n",
       "      <td>***</td>\n",
       "      <td>3.612</td>\n",
       "      <td>4.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-2.022</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.182</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.978</td>\n",
       "      <td></td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-1.258</td>\n",
       "      <td>0.208</td>\n",
       "      <td></td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.811</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.56</td>\n",
       "      <td></td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.213</td>\n",
       "      <td>0.832</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.983</td>\n",
       "      <td></td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.688</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.069</td>\n",
       "      <td>1.021</td>\n",
       "      <td>0.307</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.069</td>\n",
       "      <td>-1.618</td>\n",
       "      <td>0.106</td>\n",
       "      <td></td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.069</td>\n",
       "      <td>-1.081</td>\n",
       "      <td>0.28</td>\n",
       "      <td></td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.786</td>\n",
       "      <td>0.432</td>\n",
       "      <td></td>\n",
       "      <td>-0.193</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.069</td>\n",
       "      <td>-1.175</td>\n",
       "      <td>0.24</td>\n",
       "      <td></td>\n",
       "      <td>-0.215</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.034</td>\n",
       "      <td>1.093</td>\n",
       "      <td>0.274</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.755</td>\n",
       "      <td></td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.98</td>\n",
       "      <td></td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1.442</td>\n",
       "      <td>0.149</td>\n",
       "      <td></td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.925</td>\n",
       "      <td></td>\n",
       "      <td>-0.274</td>\n",
       "      <td>0.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.044</td>\n",
       "      <td>1.281</td>\n",
       "      <td>0.2</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.645</td>\n",
       "      <td></td>\n",
       "      <td>-0.207</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>-0.292</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-2.349</td>\n",
       "      <td>0.0188</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.181</td>\n",
       "      <td>0.857</td>\n",
       "      <td></td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.703</td>\n",
       "      <td>0.482</td>\n",
       "      <td></td>\n",
       "      <td>-0.134</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.719</td>\n",
       "      <td>0.472</td>\n",
       "      <td></td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-1.916</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.356</td>\n",
       "      <td>0.175</td>\n",
       "      <td></td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.050</td>\n",
       "      <td>3.911</td>\n",
       "      <td>9.19e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.069</td>\n",
       "      <td>9.539</td>\n",
       "      <td>1.45e-21</td>\n",
       "      <td>***</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.040</td>\n",
       "      <td>7.739</td>\n",
       "      <td>1e-14</td>\n",
       "      <td>***</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>A_exposure:norm_c</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2.376</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>**</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>B_protection:norm_c</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.931</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A_exposure:B_protection:norm_c</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.073</td>\n",
       "      <td>4.993</td>\n",
       "      <td>5.96e-07</td>\n",
       "      <td>***</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.391</td>\n",
       "      <td>0.696</td>\n",
       "      <td></td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.398</td>\n",
       "      <td></td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.834   0.113  33.916   \n",
       "1                                 CAT(gender)[T.Male] -0.070   0.035  -2.022   \n",
       "2                           CAT(gender)[T.Other/PNTS] -0.005   0.182  -0.028   \n",
       "3                   CAT(region)[T.Non-capital region] -0.043   0.034  -1.258   \n",
       "4                         CAT(education)[T.Graduate+]  0.014   0.058   0.239   \n",
       "5               CAT(education)[T.High school or less]  0.028   0.048   0.583   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.009   0.042  -0.213   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.001   0.067  -0.021   \n",
       "8                      CAT(monthly_income_band)[T.Q3]  0.028   0.069   0.402   \n",
       "9                      CAT(monthly_income_band)[T.Q4]  0.071   0.069   1.021   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.111   0.069  -1.618   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.075   0.069  -1.081   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.055   0.070  -0.786   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.081   0.069  -1.175   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.038   0.034   1.093   \n",
       "15               CAT(marital_status)[T.Never married]  0.012   0.039   0.312   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/...  0.001   0.058   0.025   \n",
       "17              CAT(employment)[T.Not in labor force]  0.113   0.078   1.442   \n",
       "18                           CAT(employment)[T.Other]  0.014   0.147   0.094   \n",
       "19                       CAT(employment)[T.Permanent]  0.057   0.044   1.281   \n",
       "20                   CAT(employment)[T.Self-employed]  0.056   0.057   0.973   \n",
       "21              CAT(employment)[T.Unemployed/seeking] -0.039   0.085  -0.460   \n",
       "22                        CAT(occupation)[T.Other/NA] -0.292   0.124  -2.349   \n",
       "23              CAT(occupation)[T.Production/On-site] -0.010   0.054  -0.181   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.035   0.050  -0.703   \n",
       "25                   CAT(occupation)[T.Public sector] -0.054   0.074  -0.719   \n",
       "26                         CAT(occupation)[T.Service] -0.104   0.054  -1.916   \n",
       "27                                         A_exposure  0.069   0.051   1.356   \n",
       "28                                       B_protection  0.196   0.050   3.911   \n",
       "29                            A_exposure:B_protection  0.660   0.069   9.539   \n",
       "30                                             norm_c  0.309   0.040   7.739   \n",
       "31                                  A_exposure:norm_c  0.130   0.055   2.376   \n",
       "32                                B_protection:norm_c -0.005   0.053  -0.087   \n",
       "33                     A_exposure:B_protection:norm_c  0.366   0.073   4.993   \n",
       "34                                                age -0.001   0.001  -0.391   \n",
       "35                                      ideology_0_10  0.007   0.009   0.845   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   3.89e-252  ***   3.612    4.055  \n",
       "1      0.0432   **  -0.138   -0.002  \n",
       "2       0.978       -0.361    0.351  \n",
       "3       0.208       -0.111    0.024  \n",
       "4       0.811       -0.099    0.127  \n",
       "5        0.56       -0.066    0.122  \n",
       "6       0.832       -0.092    0.074  \n",
       "7       0.983       -0.133    0.130  \n",
       "8       0.688       -0.107    0.162  \n",
       "9       0.307       -0.065    0.206  \n",
       "10      0.106       -0.246    0.023  \n",
       "11       0.28       -0.210    0.061  \n",
       "12      0.432       -0.193    0.083  \n",
       "13       0.24       -0.215    0.054  \n",
       "14      0.274       -0.030    0.105  \n",
       "15      0.755       -0.064    0.088  \n",
       "16       0.98       -0.111    0.114  \n",
       "17      0.149       -0.041    0.267  \n",
       "18      0.925       -0.274    0.302  \n",
       "19        0.2       -0.030    0.143  \n",
       "20       0.33       -0.057    0.168  \n",
       "21      0.645       -0.207    0.128  \n",
       "22     0.0188   **  -0.535   -0.048  \n",
       "23      0.857       -0.116    0.096  \n",
       "24      0.482       -0.134    0.063  \n",
       "25      0.472       -0.200    0.092  \n",
       "26     0.0554    *  -0.210    0.002  \n",
       "27      0.175       -0.031    0.168  \n",
       "28   9.19e-05  ***   0.098    0.295  \n",
       "29   1.45e-21  ***   0.525    0.796  \n",
       "30      1e-14  ***   0.231    0.387  \n",
       "31     0.0175   **   0.023    0.237  \n",
       "32      0.931       -0.109    0.099  \n",
       "33   5.96e-07  ***   0.222    0.509  \n",
       "34      0.696       -0.003    0.002  \n",
       "35      0.398       -0.010    0.024  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 3 (DV3: Costly policy support) — **Extension: Translation into costly policy support**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (Figure panel a): DV ~ A + B + controls**  \n",
       "N = 3,000 | R² = 0.073 | Adj. R² = 0.063"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.038</td>\n",
       "      <td>7.598</td>\n",
       "      <td>3e-14</td>\n",
       "      <td>***</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.038</td>\n",
       "      <td>10.709</td>\n",
       "      <td>9.26e-27</td>\n",
       "      <td>***</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.475</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            term   coef  se_HC3       t         p  sig  ci_low  ci_high\n",
       "27    A_exposure  0.286   0.038   7.598     3e-14  ***   0.212    0.360\n",
       "28  B_protection  0.402   0.038  10.709  9.26e-27  ***   0.328    0.475"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 3,000 | R² = 0.073 | Adj. R² = 0.063"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.855</td>\n",
       "      <td>0.122</td>\n",
       "      <td>31.604</td>\n",
       "      <td>3.24e-219</td>\n",
       "      <td>***</td>\n",
       "      <td>3.616</td>\n",
       "      <td>4.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.829</td>\n",
       "      <td>0.407</td>\n",
       "      <td></td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.185</td>\n",
       "      <td>1.695</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.049</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.557</td>\n",
       "      <td></td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.566</td>\n",
       "      <td></td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.812</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.482</td>\n",
       "      <td>0.63</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.844</td>\n",
       "      <td></td>\n",
       "      <td>-0.161</td>\n",
       "      <td>0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>0.375</td>\n",
       "      <td></td>\n",
       "      <td>-0.215</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.828</td>\n",
       "      <td></td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-0.372</td>\n",
       "      <td>0.71</td>\n",
       "      <td></td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-1.972</td>\n",
       "      <td>0.0486</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>0.824</td>\n",
       "      <td></td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.132</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-1.740</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.280</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.812</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.586</td>\n",
       "      <td></td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-1.098</td>\n",
       "      <td>0.272</td>\n",
       "      <td></td>\n",
       "      <td>-0.190</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.471</td>\n",
       "      <td></td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.149</td>\n",
       "      <td>-1.546</td>\n",
       "      <td>0.122</td>\n",
       "      <td></td>\n",
       "      <td>-0.522</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.95</td>\n",
       "      <td></td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-1.276</td>\n",
       "      <td>0.202</td>\n",
       "      <td></td>\n",
       "      <td>-0.198</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.293</td>\n",
       "      <td>0.769</td>\n",
       "      <td></td>\n",
       "      <td>-0.202</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.477</td>\n",
       "      <td></td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.688</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-1.137</td>\n",
       "      <td>0.255</td>\n",
       "      <td></td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.367</td>\n",
       "      <td></td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-1.154</td>\n",
       "      <td>0.249</td>\n",
       "      <td></td>\n",
       "      <td>-0.181</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.038</td>\n",
       "      <td>7.598</td>\n",
       "      <td>3e-14</td>\n",
       "      <td>***</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.038</td>\n",
       "      <td>10.709</td>\n",
       "      <td>9.26e-27</td>\n",
       "      <td>***</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.822</td>\n",
       "      <td>0.411</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-5.502</td>\n",
       "      <td>3.76e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.855   0.122  31.604   \n",
       "1                                 CAT(gender)[T.Male] -0.031   0.038  -0.829   \n",
       "2                           CAT(gender)[T.Other/PNTS]  0.313   0.185   1.695   \n",
       "3                   CAT(region)[T.Non-capital region]  0.022   0.038   0.587   \n",
       "4                         CAT(education)[T.Graduate+]  0.036   0.062   0.575   \n",
       "5               CAT(education)[T.High school or less]  0.012   0.052   0.238   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.022   0.047  -0.482   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.015   0.074  -0.197   \n",
       "8                      CAT(monthly_income_band)[T.Q3] -0.067   0.076  -0.887   \n",
       "9                      CAT(monthly_income_band)[T.Q4] -0.017   0.076  -0.217   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.029   0.077  -0.372   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.148   0.075  -1.972   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.016   0.073  -0.223   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.132   0.076  -1.740   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.009   0.038   0.238   \n",
       "15               CAT(marital_status)[T.Never married]  0.023   0.042   0.545   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.068   0.062  -1.098   \n",
       "17              CAT(employment)[T.Not in labor force]  0.060   0.083   0.722   \n",
       "18                           CAT(employment)[T.Other] -0.230   0.149  -1.546   \n",
       "19                       CAT(employment)[T.Permanent]  0.003   0.048   0.063   \n",
       "20                   CAT(employment)[T.Self-employed] -0.078   0.061  -1.276   \n",
       "21              CAT(employment)[T.Unemployed/seeking] -0.026   0.090  -0.293   \n",
       "22                        CAT(occupation)[T.Other/NA]  0.090   0.126   0.711   \n",
       "23              CAT(occupation)[T.Production/On-site]  0.023   0.058   0.402   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.062   0.055  -1.137   \n",
       "25                   CAT(occupation)[T.Public sector]  0.074   0.082   0.902   \n",
       "26                         CAT(occupation)[T.Service] -0.067   0.058  -1.154   \n",
       "27                                         A_exposure  0.286   0.038   7.598   \n",
       "28                                       B_protection  0.402   0.038  10.709   \n",
       "29                                                age -0.001   0.002  -0.822   \n",
       "30                                      ideology_0_10 -0.048   0.009  -5.502   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   3.24e-219  ***   3.616    4.094  \n",
       "1       0.407       -0.105    0.043  \n",
       "2      0.0901    *  -0.049    0.675  \n",
       "3       0.557       -0.052    0.096  \n",
       "4       0.566       -0.086    0.158  \n",
       "5       0.812       -0.089    0.113  \n",
       "6        0.63       -0.114    0.069  \n",
       "7       0.844       -0.161    0.131  \n",
       "8       0.375       -0.215    0.081  \n",
       "9       0.828       -0.166    0.133  \n",
       "10       0.71       -0.180    0.123  \n",
       "11     0.0486   **  -0.295   -0.001  \n",
       "12      0.824       -0.159    0.126  \n",
       "13     0.0818    *  -0.280    0.017  \n",
       "14      0.812       -0.065    0.083  \n",
       "15      0.586       -0.060    0.106  \n",
       "16      0.272       -0.190    0.053  \n",
       "17      0.471       -0.102    0.221  \n",
       "18      0.122       -0.522    0.062  \n",
       "19       0.95       -0.090    0.096  \n",
       "20      0.202       -0.198    0.042  \n",
       "21      0.769       -0.202    0.149  \n",
       "22      0.477       -0.158    0.337  \n",
       "23      0.688       -0.091    0.138  \n",
       "24      0.255       -0.170    0.045  \n",
       "25      0.367       -0.086    0.233  \n",
       "26      0.249       -0.181    0.047  \n",
       "27      3e-14  ***   0.212    0.360  \n",
       "28   9.26e-27  ***   0.328    0.475  \n",
       "29      0.411       -0.004    0.002  \n",
       "30   3.76e-08  ***  -0.065   -0.031  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Figure panel b): DV ~ A×B + controls**  \n",
       "N = 3,000 | R² = 0.088 | Adj. R² = 0.079"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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    },
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.074</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.24e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.674</td>\n",
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      "text/plain": [
       "                       term   coef  se_HC3    t         p  sig  ci_low  \\\n",
       "29  A_exposure:B_protection  0.528   0.074  7.1  1.24e-12  ***   0.383   \n",
       "\n",
       "    ci_high  \n",
       "29    0.674  "
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    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 3,000 | R² = 0.088 | Adj. R² = 0.079"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.984</td>\n",
       "      <td>0.123</td>\n",
       "      <td>32.332</td>\n",
       "      <td>2.47e-229</td>\n",
       "      <td>***</td>\n",
       "      <td>3.742</td>\n",
       "      <td>4.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-1.037</td>\n",
       "      <td>0.3</td>\n",
       "      <td></td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.181</td>\n",
       "      <td>1.757</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.56</td>\n",
       "      <td></td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.59</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.864</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-0.527</td>\n",
       "      <td>0.598</td>\n",
       "      <td></td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.365</td>\n",
       "      <td>0.715</td>\n",
       "      <td></td>\n",
       "      <td>-0.172</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.874</td>\n",
       "      <td>0.382</td>\n",
       "      <td></td>\n",
       "      <td>-0.213</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.845</td>\n",
       "      <td></td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-0.410</td>\n",
       "      <td>0.682</td>\n",
       "      <td></td>\n",
       "      <td>-0.182</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-2.063</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.323</td>\n",
       "      <td>0.747</td>\n",
       "      <td></td>\n",
       "      <td>-0.165</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-1.983</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.298</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.727</td>\n",
       "      <td></td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.57</td>\n",
       "      <td></td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.986</td>\n",
       "      <td>0.324</td>\n",
       "      <td></td>\n",
       "      <td>-0.183</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.461</td>\n",
       "      <td></td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.148</td>\n",
       "      <td>-1.357</td>\n",
       "      <td>0.175</td>\n",
       "      <td></td>\n",
       "      <td>-0.492</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.844</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>0.277</td>\n",
       "      <td></td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.894</td>\n",
       "      <td></td>\n",
       "      <td>-0.189</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.416</td>\n",
       "      <td></td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.668</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-1.169</td>\n",
       "      <td>0.243</td>\n",
       "      <td></td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.367</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.938</td>\n",
       "      <td>0.348</td>\n",
       "      <td></td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.842</td>\n",
       "      <td></td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.053</td>\n",
       "      <td>2.607</td>\n",
       "      <td>0.00915</td>\n",
       "      <td>***</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.074</td>\n",
       "      <td>7.100</td>\n",
       "      <td>1.24e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>0.461</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-5.532</td>\n",
       "      <td>3.16e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.984   0.123  32.332   \n",
       "1                                 CAT(gender)[T.Male] -0.039   0.037  -1.037   \n",
       "2                           CAT(gender)[T.Other/PNTS]  0.319   0.181   1.757   \n",
       "3                   CAT(region)[T.Non-capital region]  0.022   0.037   0.582   \n",
       "4                         CAT(education)[T.Graduate+]  0.033   0.062   0.538   \n",
       "5               CAT(education)[T.High school or less]  0.009   0.051   0.172   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.024   0.046  -0.527   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.027   0.074  -0.365   \n",
       "8                      CAT(monthly_income_band)[T.Q3] -0.066   0.075  -0.874   \n",
       "9                      CAT(monthly_income_band)[T.Q4] -0.015   0.076  -0.195   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.032   0.077  -0.410   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.154   0.075  -2.063   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.023   0.072  -0.323   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.150   0.076  -1.983   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.013   0.037   0.349   \n",
       "15               CAT(marital_status)[T.Never married]  0.024   0.042   0.568   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.061   0.062  -0.986   \n",
       "17              CAT(employment)[T.Not in labor force]  0.060   0.081   0.738   \n",
       "18                           CAT(employment)[T.Other] -0.201   0.148  -1.357   \n",
       "19                       CAT(employment)[T.Permanent]  0.009   0.047   0.197   \n",
       "20                   CAT(employment)[T.Self-employed] -0.066   0.061  -1.088   \n",
       "21              CAT(employment)[T.Unemployed/seeking] -0.012   0.090  -0.133   \n",
       "22                        CAT(occupation)[T.Other/NA]  0.101   0.125   0.813   \n",
       "23              CAT(occupation)[T.Production/On-site]  0.025   0.058   0.429   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.063   0.054  -1.169   \n",
       "25                   CAT(occupation)[T.Public sector]  0.073   0.081   0.902   \n",
       "26                         CAT(occupation)[T.Service] -0.054   0.058  -0.938   \n",
       "27                                         A_exposure  0.011   0.053   0.199   \n",
       "28                                       B_protection  0.138   0.053   2.607   \n",
       "29                            A_exposure:B_protection  0.528   0.074   7.100   \n",
       "30                                                age -0.001   0.001  -0.737   \n",
       "31                                      ideology_0_10 -0.048   0.009  -5.532   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   2.47e-229  ***   3.742    4.225  \n",
       "1         0.3       -0.112    0.035  \n",
       "2      0.0789    *  -0.037    0.675  \n",
       "3        0.56       -0.051    0.095  \n",
       "4        0.59       -0.088    0.155  \n",
       "5       0.864       -0.091    0.109  \n",
       "6       0.598       -0.115    0.066  \n",
       "7       0.715       -0.172    0.118  \n",
       "8       0.382       -0.213    0.081  \n",
       "9       0.845       -0.164    0.134  \n",
       "10      0.682       -0.182    0.119  \n",
       "11     0.0391   **  -0.300   -0.008  \n",
       "12      0.747       -0.165    0.118  \n",
       "13     0.0473   **  -0.298   -0.002  \n",
       "14      0.727       -0.060    0.086  \n",
       "15       0.57       -0.059    0.106  \n",
       "16      0.324       -0.183    0.061  \n",
       "17      0.461       -0.100    0.220  \n",
       "18      0.175       -0.492    0.089  \n",
       "19      0.844       -0.083    0.102  \n",
       "20      0.277       -0.185    0.053  \n",
       "21      0.894       -0.189    0.165  \n",
       "22      0.416       -0.143    0.346  \n",
       "23      0.668       -0.089    0.138  \n",
       "24      0.243       -0.169    0.043  \n",
       "25      0.367       -0.085    0.231  \n",
       "26      0.348       -0.167    0.059  \n",
       "27      0.842       -0.094    0.115  \n",
       "28    0.00915  ***   0.034    0.241  \n",
       "29   1.24e-12  ***   0.383    0.674  \n",
       "30      0.461       -0.004    0.002  \n",
       "31   3.16e-08  ***  -0.065   -0.031  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Figure panel c): DV ~ A×B×Norm + controls**  \n",
       "N = 3,000 | R² = 0.116 | Adj. R² = 0.105"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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    },
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
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       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A_exposure:B_protection:norm_c</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1.888</td>\n",
       "      <td>0.059</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.303</td>\n",
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      "text/plain": [
       "                              term   coef  se_HC3      t      p sig  ci_low  \\\n",
       "33  A_exposure:B_protection:norm_c  0.149   0.079  1.888  0.059   *  -0.006   \n",
       "\n",
       "    ci_high  \n",
       "33    0.303  "
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    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 3,000 | R² = 0.116 | Adj. R² = 0.105"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
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       "      <th>t</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.819</td>\n",
       "      <td>0.125</td>\n",
       "      <td>30.667</td>\n",
       "      <td>1.58e-206</td>\n",
       "      <td>***</td>\n",
       "      <td>3.575</td>\n",
       "      <td>4.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-0.748</td>\n",
       "      <td>0.454</td>\n",
       "      <td></td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(gender)[T.Other/PNTS]</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.187</td>\n",
       "      <td>1.765</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(region)[T.Non-capital region]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.539</td>\n",
       "      <td></td>\n",
       "      <td>-0.049</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Graduate+]</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.375</td>\n",
       "      <td></td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.High school or less]</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.748</td>\n",
       "      <td></td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.Some college (2-yr/Enrolled)]</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-0.426</td>\n",
       "      <td>0.67</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.481</td>\n",
       "      <td>0.631</td>\n",
       "      <td></td>\n",
       "      <td>-0.178</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.973</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.220</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.877</td>\n",
       "      <td></td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-0.369</td>\n",
       "      <td>0.712</td>\n",
       "      <td></td>\n",
       "      <td>-0.179</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-2.192</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.308</td>\n",
       "      <td>-0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.814</td>\n",
       "      <td></td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.144</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-1.937</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeownership)[T.Renter/Other]</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.854</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Never married]</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.521</td>\n",
       "      <td></td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Other (widowed/divorced/...</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.721</td>\n",
       "      <td>0.471</td>\n",
       "      <td></td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(employment)[T.Not in labor force]</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.354</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment)[T.Other]</td>\n",
       "      <td>-0.174</td>\n",
       "      <td>0.145</td>\n",
       "      <td>-1.201</td>\n",
       "      <td>0.23</td>\n",
       "      <td></td>\n",
       "      <td>-0.459</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment)[T.Permanent]</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.732</td>\n",
       "      <td></td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment)[T.Self-employed]</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-0.876</td>\n",
       "      <td>0.381</td>\n",
       "      <td></td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAT(employment)[T.Unemployed/seeking]</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>0.726</td>\n",
       "      <td></td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAT(occupation)[T.Other/NA]</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.385</td>\n",
       "      <td></td>\n",
       "      <td>-0.137</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAT(occupation)[T.Production/On-site]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.486</td>\n",
       "      <td></td>\n",
       "      <td>-0.072</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAT(occupation)[T.Professional/Managerial]</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>0.399</td>\n",
       "      <td></td>\n",
       "      <td>-0.151</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAT(occupation)[T.Public sector]</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.080</td>\n",
       "      <td>1.156</td>\n",
       "      <td>0.248</td>\n",
       "      <td></td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAT(occupation)[T.Service]</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.574</td>\n",
       "      <td>0.566</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A_exposure</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.744</td>\n",
       "      <td></td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B_protection</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.053</td>\n",
       "      <td>2.805</td>\n",
       "      <td>0.00503</td>\n",
       "      <td>***</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>A_exposure:B_protection</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.074</td>\n",
       "      <td>6.948</td>\n",
       "      <td>3.71e-12</td>\n",
       "      <td>***</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.042</td>\n",
       "      <td>1.776</td>\n",
       "      <td>0.0757</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>A_exposure:norm_c</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.452</td>\n",
       "      <td></td>\n",
       "      <td>-0.071</td>\n",
       "      <td>0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>B_protection:norm_c</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.397</td>\n",
       "      <td></td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A_exposure:B_protection:norm_c</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1.888</td>\n",
       "      <td>0.059</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.541</td>\n",
       "      <td>0.588</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ideology_0_10</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-2.731</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 term   coef  se_HC3       t  \\\n",
       "0                                           Intercept  3.819   0.125  30.667   \n",
       "1                                 CAT(gender)[T.Male] -0.028   0.037  -0.748   \n",
       "2                           CAT(gender)[T.Other/PNTS]  0.331   0.187   1.765   \n",
       "3                   CAT(region)[T.Non-capital region]  0.023   0.037   0.614   \n",
       "4                         CAT(education)[T.Graduate+]  0.055   0.062   0.888   \n",
       "5               CAT(education)[T.High school or less]  0.016   0.050   0.321   \n",
       "6      CAT(education)[T.Some college (2-yr/Enrolled)] -0.019   0.046  -0.426   \n",
       "7                      CAT(monthly_income_band)[T.Q2] -0.035   0.073  -0.481   \n",
       "8                      CAT(monthly_income_band)[T.Q3] -0.073   0.075  -0.973   \n",
       "9                      CAT(monthly_income_band)[T.Q4] -0.012   0.075  -0.155   \n",
       "10                     CAT(monthly_income_band)[T.Q5] -0.028   0.077  -0.369   \n",
       "11                     CAT(monthly_income_band)[T.Q6] -0.163   0.074  -2.192   \n",
       "12                     CAT(monthly_income_band)[T.Q7] -0.017   0.072  -0.235   \n",
       "13                     CAT(monthly_income_band)[T.Q8] -0.144   0.074  -1.937   \n",
       "14                 CAT(homeownership)[T.Renter/Other]  0.007   0.037   0.184   \n",
       "15               CAT(marital_status)[T.Never married]  0.027   0.041   0.641   \n",
       "16  CAT(marital_status)[T.Other (widowed/divorced/... -0.045   0.062  -0.721   \n",
       "17              CAT(employment)[T.Not in labor force]  0.075   0.081   0.926   \n",
       "18                           CAT(employment)[T.Other] -0.174   0.145  -1.201   \n",
       "19                       CAT(employment)[T.Permanent]  0.016   0.047   0.342   \n",
       "20                   CAT(employment)[T.Self-employed] -0.052   0.060  -0.876   \n",
       "21              CAT(employment)[T.Unemployed/seeking] -0.032   0.090  -0.350   \n",
       "22                        CAT(occupation)[T.Other/NA]  0.109   0.126   0.869   \n",
       "23              CAT(occupation)[T.Production/On-site]  0.040   0.057   0.696   \n",
       "24         CAT(occupation)[T.Professional/Managerial] -0.045   0.054  -0.843   \n",
       "25                   CAT(occupation)[T.Public sector]  0.092   0.080   1.156   \n",
       "26                         CAT(occupation)[T.Service] -0.033   0.057  -0.574   \n",
       "27                                         A_exposure  0.017   0.053   0.327   \n",
       "28                                       B_protection  0.148   0.053   2.805   \n",
       "29                            A_exposure:B_protection  0.511   0.074   6.948   \n",
       "30                                             norm_c  0.075   0.042   1.776   \n",
       "31                                  A_exposure:norm_c  0.044   0.059   0.753   \n",
       "32                                B_protection:norm_c  0.047   0.056   0.847   \n",
       "33                     A_exposure:B_protection:norm_c  0.149   0.079   1.888   \n",
       "34                                                age -0.001   0.001  -0.541   \n",
       "35                                      ideology_0_10 -0.025   0.009  -2.731   \n",
       "\n",
       "            p  sig  ci_low  ci_high  \n",
       "0   1.58e-206  ***   3.575    4.063  \n",
       "1       0.454       -0.100    0.045  \n",
       "2      0.0775    *  -0.036    0.698  \n",
       "3       0.539       -0.049    0.095  \n",
       "4       0.375       -0.067    0.177  \n",
       "5       0.748       -0.082    0.114  \n",
       "6        0.67       -0.109    0.070  \n",
       "7       0.631       -0.178    0.108  \n",
       "8        0.33       -0.220    0.074  \n",
       "9       0.877       -0.159    0.136  \n",
       "10      0.712       -0.179    0.122  \n",
       "11     0.0283   **  -0.308   -0.017  \n",
       "12      0.814       -0.158    0.124  \n",
       "13     0.0527    *  -0.290    0.002  \n",
       "14      0.854       -0.065    0.079  \n",
       "15      0.521       -0.055    0.108  \n",
       "16      0.471       -0.166    0.077  \n",
       "17      0.354       -0.083    0.233  \n",
       "18       0.23       -0.459    0.110  \n",
       "19      0.732       -0.075    0.107  \n",
       "20      0.381       -0.170    0.065  \n",
       "21      0.726       -0.208    0.145  \n",
       "22      0.385       -0.137    0.356  \n",
       "23      0.486       -0.072    0.152  \n",
       "24      0.399       -0.151    0.060  \n",
       "25      0.248       -0.064    0.248  \n",
       "26      0.566       -0.145    0.079  \n",
       "27      0.744       -0.087    0.122  \n",
       "28    0.00503  ***   0.044    0.251  \n",
       "29   3.71e-12  ***   0.367    0.655  \n",
       "30     0.0757    *  -0.008    0.157  \n",
       "31      0.452       -0.071    0.160  \n",
       "32      0.397       -0.062    0.157  \n",
       "33      0.059    *  -0.006    0.303  \n",
       "34      0.588       -0.004    0.002  \n",
       "35    0.00632  ***  -0.043   -0.007  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "✅ **Done. DV1: 2 specs + DV2/DV3: 3 specs = 8 full regression tables displayed.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================================\n",
    "# 0) PATH\n",
    "# ============================================================\n",
    "DATA_DIR  = r\"E:\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "# ============================================================\n",
    "# 1) Variables (Paper 1)\n",
    "# ============================================================\n",
    "A = \"A_exposure\"       # 1=High exposure, 0=Low\n",
    "B = \"B_protection\"     # 1=Official protection present, 0=Absent\n",
    "\n",
    "DVs = {\n",
    "    \"Figure 1 (DV1: Structural attribution)\": \"DV1_structural_attribution\",\n",
    "    \"Figure 2 (DV2: Public responsibility)\":  \"DV2_public_responsibility\",\n",
    "    \"Figure 3 (DV3: Costly policy support)\":  \"DV3_costly_policy_support\",\n",
    "}\n",
    "\n",
    "DV_TO_HYP = {\n",
    "    \"DV1_structural_attribution\": \"Recognition stage (mechanism)\",\n",
    "    \"DV2_public_responsibility\":  \"H1/H2 core: Translation into public responsibility\",\n",
    "    \"DV3_costly_policy_support\":  \"Extension: Translation into costly policy support\",\n",
    "}\n",
    "\n",
    "# Norm index or items\n",
    "PREFERRED_NORM_ITEMS = [\"Norm1\", \"Norm2\", \"Norm3\"]   # Norm2 is reverse-coded\n",
    "NORM_INDEX_COL = \"Norm_collective_responsibility\"\n",
    "\n",
    "# ============================================================\n",
    "# 2) Pre-processing: ideology integer, income band, norm index + reverse coding\n",
    "# ============================================================\n",
    "if \"ideology_0_10\" in df.columns:\n",
    "    df[\"ideology_0_10\"] = pd.to_numeric(df[\"ideology_0_10\"], errors=\"coerce\")\n",
    "    df[\"ideology_0_10\"] = df[\"ideology_0_10\"].round().clip(0, 10).astype(\"Int64\")\n",
    "\n",
    "if \"monthly_income_band\" not in df.columns:\n",
    "    if \"income_million_krw\" in df.columns:\n",
    "        df[\"monthly_income_band\"] = pd.qcut(\n",
    "            df[\"income_million_krw\"], q=8, labels=[f\"Q{i}\" for i in range(1, 9)]\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(\"monthly_income_band도 없고 income_million_krw도 없습니다. 소득 변수명을 확인하세요.\")\n",
    "\n",
    "def reverse_1to7(x):\n",
    "    return 8 - x\n",
    "\n",
    "if NORM_INDEX_COL in df.columns:\n",
    "    df[\"norm_index\"] = pd.to_numeric(df[NORM_INDEX_COL], errors=\"coerce\")\n",
    "else:\n",
    "    if all(c in df.columns for c in PREFERRED_NORM_ITEMS):\n",
    "        n1 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[0]], errors=\"coerce\")\n",
    "        n2 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[1]], errors=\"coerce\")\n",
    "        n3 = pd.to_numeric(df[PREFERRED_NORM_ITEMS[2]], errors=\"coerce\")\n",
    "        n2r = reverse_1to7(n2)\n",
    "        df[\"norm_index\"] = pd.concat([n1, n2r, n3], axis=1).mean(axis=1)\n",
    "    else:\n",
    "        raise ValueError(\n",
    "            f\"Norm index를 만들 수 없습니다. {NORM_INDEX_COL}가 없고, \"\n",
    "            f\"{PREFERRED_NORM_ITEMS}도 없습니다. df.xlsx의 Norm 문항 변수명을 확인하세요.\"\n",
    "        )\n",
    "\n",
    "df[\"norm_c\"] = (df[\"norm_index\"] - df[\"norm_index\"].mean()).fillna(0.0)\n",
    "\n",
    "# ============================================================\n",
    "# 3) Controls (use CAT() to avoid patsy C() collision)\n",
    "# ============================================================\n",
    "CAT = pb.C\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_income_band)\",\n",
    "    \"CAT(homeownership)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(employment)\",\n",
    "    \"CAT(occupation)\",\n",
    "    \"ideology_0_10\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "# ============================================================\n",
    "# 4) Helpers (full table + pretty printing)\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def full_table(m):\n",
    "    out = pd.DataFrame({\n",
    "        \"term\": m.params.index,\n",
    "        \"coef\": m.params.values,\n",
    "        \"se_HC3\": m.bse.values,\n",
    "        \"t\": m.tvalues.values,\n",
    "        \"p\": m.pvalues.values\n",
    "    })\n",
    "    out[\"ci_low\"]  = out[\"coef\"] - 1.96*out[\"se_HC3\"]\n",
    "    out[\"ci_high\"] = out[\"coef\"] + 1.96*out[\"se_HC3\"]\n",
    "    return out\n",
    "\n",
    "def add_sig_stars(p):\n",
    "    if p < 0.01: return \"***\"\n",
    "    if p < 0.05: return \"**\"\n",
    "    if p < 0.1:  return \"*\"\n",
    "    return \"\"\n",
    "\n",
    "def pretty(df_in, digits=3):\n",
    "    df2 = df_in.copy()\n",
    "    df2[\"sig\"] = df2[\"p\"].apply(add_sig_stars)\n",
    "    for c in [\"coef\",\"se_HC3\",\"t\",\"ci_low\",\"ci_high\"]:\n",
    "        df2[c] = df2[c].map(lambda x: round(float(x), digits))\n",
    "    df2[\"p\"] = df2[\"p\"].map(lambda x: f\"{x:.3g}\")\n",
    "    return df2[[\"term\",\"coef\",\"se_HC3\",\"t\",\"p\",\"sig\",\"ci_low\",\"ci_high\"]]\n",
    "\n",
    "def model_stats_line(m):\n",
    "    return f\"N = {int(m.nobs):,} | R² = {m.rsquared:.3f} | Adj. R² = {m.rsquared_adj:.3f}\"\n",
    "\n",
    "# Key terms per spec (so tables are consistent with figure panels)\n",
    "KEY_TERMS_A = [A, B]                        # Panel (a): main effects model\n",
    "KEY_TERMS_B = [f\"{A}:{B}\"]                  # Panel (b): interaction\n",
    "KEY_TERMS_C = [f\"{A}:{B}:norm_c\"]           # Panel (c): 3-way interaction\n",
    "\n",
    "def show_model_key_terms(title, m, key_terms):\n",
    "    display(Markdown(f\"**{title}**  \\n{model_stats_line(m)}\"))\n",
    "    tbl = pretty(full_table(m))\n",
    "    display(Markdown(\"*Key terms:*\"))\n",
    "    display(tbl[tbl[\"term\"].isin(key_terms)])\n",
    "\n",
    "def show_model(title, m):\n",
    "    display(Markdown(f\"**{title}**  \\n{model_stats_line(m)}\"))\n",
    "    display(pretty(full_table(m)))\n",
    "\n",
    "# ============================================================\n",
    "# 5) Run + Display\n",
    "#   Spec A matches Figure panel (a): A + B + controls\n",
    "#   Spec B matches Figure panel (b): A*B + controls (report A:B)\n",
    "#   Spec C matches Figure panel (c): A*B*norm_c + controls (report A:B:norm_c)\n",
    "#\n",
    "#   For DV1, we run Spec A and Spec B only (Figure 1 has no panel c)\n",
    "# ============================================================\n",
    "for fig_name, dv in DVs.items():\n",
    "    hyp = DV_TO_HYP.get(dv, \"Hypothesis\")\n",
    "    display(Markdown(f\"## {fig_name} — **{hyp}**\"))\n",
    "\n",
    "    # ---- Spec A (Figure panel a)\n",
    "    fA = f\"{dv} ~ {A} + {B} + {CONTROLS}\"\n",
    "    mA = fit_ols(fA, df)\n",
    "    show_model_key_terms(\"Spec A (Figure panel a): DV ~ A + B + controls\", mA, KEY_TERMS_A)\n",
    "    show_model(\"Full results — Spec A\", mA)\n",
    "\n",
    "    # ---- Spec B (Figure panel b)\n",
    "    fB = f\"{dv} ~ {A}*{B} + {CONTROLS}\"\n",
    "    mB = fit_ols(fB, df)\n",
    "    show_model_key_terms(\"Spec B (Figure panel b): DV ~ A×B + controls\", mB, KEY_TERMS_B)\n",
    "    show_model(\"Full results — Spec B\", mB)\n",
    "\n",
    "    # ---- Spec C (Figure panel c) for DV2/DV3 only\n",
    "    if dv != \"DV1_structural_attribution\":\n",
    "        fC = f\"{dv} ~ {A}*{B}*norm_c + {CONTROLS}\"\n",
    "        mC = fit_ols(fC, df)\n",
    "        show_model_key_terms(\"Spec C (Figure panel c): DV ~ A×B×Norm + controls\", mC, KEY_TERMS_C)\n",
    "        show_model(\"Full results — Spec C\", mC)\n",
    "\n",
    "display(Markdown(\"✅ **Done. DV1: 2 specs + DV2/DV3: 3 specs = 8 full regression tables displayed.**\"))"
   ]
  },
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